Posts in category: Microsoft AI

Transcript

PETER LEE: “… This is the moment for broad, thoughtful consideration of how to ensure maximal safety and also maximum access. Like any medical tool, AI needs those guardrails to keep patients as safe as possible. But it’s a tricky balance: those safety measures must not mean that the great advantages that we document in this book end up unavailable to many who could benefit from them. One of the most exciting aspects of this moment is that the new AI could accelerate healthcare in a direction that is better for patients, all patients, and providers as well—if they have access.” 

This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.    

Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?     

In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here. 

The passage I read at the top there is from Chapter 9, “Safety First.” 

One needs only to look at examples such as laws mandating seatbelts in cars and, more recently, internet regulation to know that policy and oversight are often playing catch-up with emerging technologies. When we were writing our book, Carey, Zak, and I didn’t claim that putting frameworks in place to allow for innovation and adoption while prioritizing inclusiveness and protecting patients from hallucination and other harms would be easy. In fact, in our writing, we posed more questions than answers in the hopes of highlighting the complexities at hand and supporting constructive discussion and action in this space.  

In this episode, I’m pleased to welcome three experts who have been thinking deeply about these matters: Laura Adams, Vardit Ravitsky, and Dr. Roxana Daneshjou.  

Laura is an expert in AI, digital health, and human-centered care. As a senior advisor at the National Academy of Medicine, or NAM, she guides strategy for the academy’s science and technology portfolio and leads the Artificial Intelligence Code of Conduct national initiative.  

Vardit is president and CEO of The Hastings Center for Bioethics, a bioethics and health policy institute. She leads research projects funded by the National Institutes of Health, is a member of the committee developing the National Academy of Medicine’s AI Code of Conduct, and is a senior lecturer at Harvard Medical School.  

Roxana is a board-certified dermatologist and an assistant professor of both dermatology and biomedical data science at Stanford University. Roxana is among the world’s thought leaders in AI, healthcare, and medicine, thanks in part to groundbreaking work on AI biases and trustworthiness. 

One of the good fortunes I’ve had in my career is the chance to work with both Laura and Vardit, mainly through our joint work with the National Academy of Medicine. They’re both incredibly thoughtful and decisive leaders working very hard to help the world of healthcare—and healthcare regulators—come to grips with generative AI. And over the past few years, I’ve become an avid reader of all of Roxana’s research papers. Her work is highly technical, super influential but also informative in a way that spans computer science, medicine, bioethics, and law.  

These three leaders—one from the medical establishment, one from the bioethics field, and the third from clinical research—provide insights into three incredibly important dimensions of the issues surrounding regulations, norms, and ethics of AI in medicine. 

Here is my interview with Laura Adams: 

LEE: Laura, I’m just incredibly honored and excited that you’re joining us here today, so welcome. 

ADAMS: Thank you, Peter, my pleasure. Excited to be here. 

LEE: So, Laura, you know, I’ve been working with you at the NAM for a while, and you are a strategic advisor at the NAM. But I think a lot of our listeners might not know too much about the National Academy of Medicine and then, within the National Academy of Medicine, what a strategic advisor does.  

So why don’t we start there. You know, how would you explain to a person’s mother or father what the National Academy of Medicine is? 

ADAMS: Sure. National Academy was formed more than 50 years ago. It was formed by the federal government, but it is not the federal government. It was formed as an independent body to advise the nation and the federal government on issues of science and primarily technology-related issues, as well.  

So with that 50 years, some probably know of the National Academy of Medicine when it was the Institute of Medicine and produced such publications as To Err is Human (opens in new tab) and Crossing the Quality Chasm (opens in new tab), both of which were seminal publications that I think had a dramatic impact on quality, safety, and how we saw our healthcare system and what we saw in terms of its potential. 

LEE: So now, for your role within NAM, what does the senior advisor do? What do you do?  

ADAMS: What I do there is in the course of leading the AI Code of Conduct project, my role there was in framing the vision for that project, really understanding what did we want it to do, what impact did we want it to make.  

So for example, some thought that it might be that we wanted everyone to use our code of conduct. And my advice on that was let’s use this as a touchstone. We want people to think about their own codes of conduct for their use of AI. That’s a valuable exercise, to decide what you value, what your aspirations are.  

I also then do a lot of the field alignment around that work. So I probably did 50 talks last year—conference presentations, webinars, different things—where the code of conduct was presented so that the awareness could be raised around it so people could see the practicality of using that tool.   
 
Especially the six commitments that were based on the idea of complex adaptive systems simple rules, where we could recall those in the heat of decision-making around AI, in the heat of application, or even in the planning and strategic thinking around it.  

LEE: All right, we’re going to want to really break into a lot of details here.  

But I would just like to rewind the clock a little bit and talk about your first encounters with AI. And there’s sort of, I guess, two eras. There’s the era of AI and machine learning before ChatGPT, before the generative AI era, and then afterwards.  

Before the era of generative AI, what was your relationship with the idea of artificial intelligence? Was it a big part of your role and something you thought about, or was it just one of many technologies that you considered? 

ADAMS: It was one of many.  

LEE: Yeah.  

ADAMS: Watching it help us evolve from predictive analytics to predictive AI, which of course I was fascinated by the fact that it could use structured and unstructured data, that it could learn from its own processes. These things were really quite remarkable, but my sense about it was that it was one of many.  

We were looking at telemedicine. We were looking at [a] variety of other things, particularly wearables and things that were affecting and empowering patients to take better care of themselves and take more … have more agency around their own care. So I saw it as one of many.  

And then the world changed in 2022, changed dramatically. 

LEE: [LAUGHS] OK. Right. OK, so November 2022, ChatGPT. Later in the spring of 2023, GPT-4. And so, you know, what were your first encounters, and what were you feeling? What were you experiencing? 

ADAMS: At the time, I was curious, and I thought, I think I’m seeing four things here that make this way different.  

And one was, and it proved to be true over time, the speed with which this evolved. And I was watching it evolve very, very quickly and thinking, this is almost, this is kind of mind blowing how fast this is getting better.  

And then this idea that, you know, we could scale this. As we were watching the early work with ambient listening, I was working with a group of physicians that were lamenting the cost and the unavailability of scribes. They wanted to use scribes. And I’m thinking, We don’t have to incur the cost of that. We don’t have to struggle with the unavailability of that type of … someone in the workforce.   

And then I started watching the ubiquity, and I thought, Oh, my gosh, this is unlike any other technology that we’ve seen. Because with electronic health records, for example, it’s had its place, but it was over here. We had another digital technology, maybe telehealth, over here. This was one, and I thought, there will be no aspect of healthcare that will be left untouched by AI. That blew my mind.  

LEE: Yeah. 

ADAMS: And then I think the last thing was the democratization. And I realized: Wow, anyone with a smartphone has access to the most powerful large language models in the world.  
 
And I thought, This, to me, is a revolution in cheap expertise. Those were the things that really began to stun me, and I just knew that we were in a way different era. 

LEE: It’s interesting that you first talked about ambient listening. Why was that of particular initial interest to you specifically? 

ADAMS: It was because one of the things that we were putting together in our code of conduct, which began pre-generative AI, was the idea that we wanted to renew the moral well-being and the sense of shared purpose to the healthcare workforce. That’s one of the six principles.  

And I knew that the cognitive burden was becoming unbearable. When we came out of COVID, it was such a huge wake-up call to understand exactly what was going on at that point of care and how challenging it had become because information overload is astonishing in and of itself. And that idea that we have so much in the way of documentation that needed to be done and how much of a clinician’s time was taken up doing that rather than doing the thing that they went into the profession to do. And that was interact with people, that was to heal, that was to develop human connection that had a healing effect, and they just … so much of the time was taken away from that activity.  

I also looked at it and because I studied diffusion of innovations theory and understand what causes something to move rapidly across a social system and get adopted, it has to have a clear relative advantage. It has to be compatible with the way that processes work. 

So I didn’t see that this was going to be a hugely disruptive activity to workflow, which is a challenge of most digital tools, is that they’re designed without that sense of, how does this impact the workflow? And then I just thought that it was going to be a front runner in adoption, and it might then start to create that tsunami, that wave of interest in this, and I don’t think I was wrong. 

LEE: I have to ask you, because I’ve been asking every guest, there must have been moments early on in the encounter with generative AI where you felt doubt or skepticism. Is that true, or did you immediately think, Wow, this is something very important? 

ADAMS: No, I did feel doubt and skepticism.  

My understanding tells me of it, and told me of it in the very beginning, that this is trained on the internet with all of its flaws. When we think about AI, we think about it being very futuristic, but it’s trained on data from the past. I’m well aware of how flawed that data, how biased that data is, mostly men, mostly white men, when we think about it during a certain age grouping of.  

So I knew that we had inherent massive flaws in the training data and that concerned me. I saw other things about it that also concerned me. I saw that … its difficulty in beginning to use it and govern it effectively.  

You really do have to put a good governance system in if you’re going to put this into a care delivery system. And I began to worry about widening a digital divide that already was a chasm. And that was between those well-resourced, usually urban, hospitals and health systems that are serving the well-resourced, and the inner-city hospital in Chicago or the rural hospital in Nebraska or the Mississippi community health center.  

LEE: Yes. So I think this skepticism about technology, new technologies, in healthcare is very well-earned. So you’ve zeroed in on this issue of technology where oftentimes we hope it’ll reduce or eliminate biases but actually seems to oftentimes have the opposite effect.  

And maybe this is a good segue then into this really super-important national effort that you’re leading on the AI code of conduct. Because in a way, I think those failures of the past and even just the idea—the promise—that technology should make a doctor or a nurse’s job easier, not harder, even that oftentimes seems not to have panned out in the way that we hope.  

And then there’s, of course, the well-known issue of hallucinations or of mistakes being made. You know, how did those things inform this effort around a code of conduct, and why a code of conduct? 

ADAMS: Those things weighed heavily on me as the initiative leader because I had been deeply involved in the spread of electronic health records, not really knowing and understanding that electronic health records were going to have the effect that they had on the providers that use them.  

Looking back now, I think that there could have been design changes, but we probably didn’t have as much involvement of providers in the design. And in some cases, we did. We just didn’t understand what it would take to work it into their workflows.  

So I wanted to be sure that the code of conduct took into consideration and made explicit some of the things that I believe would have helped us had we had those guardrails or those guidelines explicit for us.  

And those are things like our first one is to protect and advance human health and connection.  

We also wanted to see things about openly sharing and monitoring because we know that for this particular technology, it’s emergent. We’re going to have to do a much better job at understanding whether what we’re doing works and works in the real world.  

So the reason for a code of conduct was we knew that … the good news, when the “here comes AI and it’s barreling toward us,” the good news was that everybody was putting together guidelines, frameworks, and principle sets. The bad news was same. That everybody was putting together their own guideline, principle, and framework set.  

And I thought back to how much I struggled when I worked in the world of health information exchange and built a statewide health information exchange and then turned to try to exchange that data across the nation and realized that we had a patchwork of privacy laws and regulations across the state; it was extremely costly to try to move data.  

And I thought we actually need, in addition to data interoperability, we need governance interoperability, where we can begin to agree on a core set of principles that will more easily allow us to move ahead and achieve some of the potential and the vision that we have for AI if we are not working with a patchwork of different guidelines, principles, and frameworks.  

So that was the impetus behind it. Of course, we again want it to be used as a touchstone, not everybody wholesale adopt what we’ve said.  

LEE: Right. 

ADAMS: We want people to think about this and think deeply about it. 

LEE: Yeah, Laura, I always am impressed with just how humble you are. You were indeed, you know, one of the prime instigators of the digitization of health records leading to electronic health record systems. And I don’t think you need to feel bad about that. That was a tremendous advance. I mean, moving a fifth of the US economy to be digital, I think, is significant.  

Also, our listeners might want to know that you led something called the Rhode Island Quality Institute, which was really, I think, maybe the, arguably, the most important early kind of examples that set a pattern for how and why health data might actually lead very directly to improvements in human health at a statewide level or at a population level. And so I think your struggles and frustrations on, you know, how to expand that nationwide, I think, are really, really informative.  

So let’s get into what these principles are, you know, what’s in the code of conduct.  

ADAMS: Yeah, the six simple rules were derived out of a larger set of principles that we pulled together. And the origin of all of this was we did a fairly extensive landscape review. We looked at least at 60 different sets of these principles, guidelines, and frameworks. We looked for areas of real convergence. We looked for areas where there was inconsistencies. And we looked for out-and-out gaps.  

The out-and-out gaps that we saw at the time were things like a dearth of references to advancing human health as the priority. Also monitoring post-implementation. So at the time, we were watching these evolve and we thought these are very significant gaps. Also, the impact on the environment was a significant gap, as well. And so when we pull that together, we developed a set of principles and cross-walked those with learning health system principles (opens in new tab).  

And then once we got that, we again wanted to distill that down into a set of commitments which we knew that people could find accessible. And we published that draft set of principles (opens in new tab) last year. And we have a new publication that will be coming out in the coming months that will be the revised set of principles and code commitments that we got because we took this out publicly. 

So we opened it up for public comment once we did the draft last year. Again, many of those times that I spoke about this, almost all of those times came with an invitation for feedback, and conversations that we had with people shaped it. And it is in no way, shape, or form a final code of conduct, this set of principles and commitments, because we see this as dynamic. But what we also knew about this was that we wanted to build this with a super solid foundation, a set of immutables, the things that don’t change at some vicissitudes or the whims of this or the whims of that. We wanted those things that were absolutely foundational.  

LEE: Yeah, so we’ll provide a link to the documents that describe the current state of this, but can we give an example of one or two of these principles and one or two of the commitments? 

ADAMS: Sure. I’ve mentioned the “protect and advance human health and connection” as the primary aim. We also want to ensure the equitable distribution of risks and benefits, and that equitable distribution of risks and benefits is something that I was referring to earlier about when I see well-resourced organizations. And one that’s particularly important to me is engaging people as partners with agency at every stage of the AI lifecycle.  

That one matters because this one talks about and speaks to the idea that we want to begin bringing in those that are affected by AI, those on whom AI is used, into the early development and conceptualization of what we want this new tool, this new application, to do. So that includes the providers that use it, the patients. And we find that when we include them—the ethicists that come along with that—we develop much better applications, much more targeted applications that do what we intend them to do in a more precise way.  

The other thing about that engaging with agency, by agency we mean that person, that participant can affect the decisions and they can affect the outcome. So it isn’t that they’re sort of a token person coming into the table and we’ll allow you to tell your story or so, but this is an active participant.  

We practiced what we preached when we developed the code of conduct, and we brought patient advocates in to work with us on the development of this, work with us on our applications, that first layer down of what the applications would look like, which is coming out in this new paper.  

We really wanted that component of this because I’m also seeing that patients are definitely not passive users of this, and they’re having an agency moment, let’s say, with generative AI because they’ve discovered a new capacity to gain information, to—in many ways—claim some autonomy in all of this.   

And I think that there is a disruption underway right now, a big one that has been in the works for many years, but it feels to me like AI may be the tipping point for that disruption of the delivery system as we know it.  

LEE: Right. I think it just exudes sort of inclusivity and thoughtfulness in the whole process. During this process, were there surprises, things that you didn’t expect? Things about AI technology itself that surprised you? 

ADAMS: The surprises that came out of this process for me, one of them was I surprised myself. We were working on the commentary paper, and Steven Lin from Stanford had strong input into that paper. And when we looked at what we thought were missing, he said, “Let’s make sure we have the environmental impact.” And I said, “Oh, really, Steven, we really want to think about things that are more directly aligned with health,” which I couldn’t believe came out of my own mouth. [LAUGHTER] 

And Steven, without saying, “Do you hear yourself?” I mean, I think he could have said that. But he was more diplomatic than that. And he persisted a bit longer and said, “I think it’s actually the greatest threat to human health.” And I said, “Of course, you’re right.” [LAUGHS] 

But that was surprising and embarrassing for me. But it was eye-opening in that even when I thought that I had understood the gaps and the using this as a touchstone. So the learning that took place and how rapidly that learning was happening among people involved in this.  

The other thing that was surprising for me was the degree at which patients became vastly facile with using it to the extent that it helped them begin to, again, build their own capacity.  

The #PatientsUseAI from Dave deBronkart—watch that one. This is more revolutionary than we think. And so I watched that, the swell of that happening, and it sort of shocked me because I was envisioning this as, again, a tool for use in the clinical setting.  

LEE: Yeah. OK, so we’re running now towards the end of our time together. And I always like to end our conversations with a more provocative topic. And I thought for you, I’d like to use the very difficult word regulation.  

And when I think about the book that Carey, Zak, and I wrote, we have a chapter on regulation, but honestly, we didn’t have ideas. We couldn’t understand how this would be regulated. And so we just defaulted to publishing a conversation about regulation with GPT-4. And in a way, I think … I don’t know that I or my coauthors were satisfied with that.  

In your mind, where do we stand two years later now when we think about the need or not to regulate AI, particularly in its application to healthcare, and where has the thinking evolved to? 

ADAMS: There are two big differences that I see in that time that has elapsed. And the first one is we have understood the insufficiency of simply making sure that AI-enabled devices are safe prior to going out into implementation settings.  

We recognize now that there’s got to be this whole other aspect of regulation and assurance that these things are functioning as intended and we have the capacity to do that in the point of care type of setting. So that’s been one of the major ones. The other thing is how wickedly challenging it is to regulate generative AI.  

I think one of the most provocative and exciting articles (opens in new tab) that I saw written recently was by Bakul Patel and David Blumenthal, who posited, should we be regulating generative AI as we do a licensed and qualified provider?  

Should it be treated in the sense that it’s got to have a certain amount of training and a foundation that’s got to pass certain tests? It has to demonstrate that it’s improving and keeping up with current literature. Does it … be responsible for mistakes that it makes in some way, shape, or form? Does it have to report its performance?  

And I’m thinking, what a provocative idea …  

LEE: Right. 

ADAMS: … but it’s worth considering. I chair the Global Opportunities Group for a regulatory and innovation AI sandbox in the UK. And we’re hard at work thinking about, how do you regulate something as unfamiliar and untamed, really, as generative AI?  

So I’d like to see us think more about this idea of sandboxes, more this idea of should we be just completely rethinking the way that we regulate. To me, that’s where the new ideas will come because the danger, of course, in regulating in the old way … first of all, we haven’t kept up over time, even with predictive AI; even with pre-generative AI, we haven’t kept up.  

And what worries me about continuing on in that same vein is that we will stifle innovation … 

LEE: Yes. 

ADAMS: … and we won’t protect from potential harms. Nobody wants an AI Chernobyl, nobody.  

LEE: Right 
 
ADAMS: But I worry that if we use those old tools on the new applications that we will not only not regulate, then we’ll stifle innovation. And when I see all of the promise coming out of this for things that we thought were unimaginable, then that would be a tragedy. 

LEE: You know, I think the other reflection I’ve had on this is the consumer aspect of it, because I think a lot of our current regulatory frameworks are geared towards experts using the technology.  

ADAMS: Yes. 

LEE: So when you have a medical device, you know you have a trained, board-certified doctor or licensed nurse using the technology. But when you’re putting things in the hands of a consumer, I think somehow the surface area of risk seems wider to me. And so I think that’s another thing that somehow our current regulatory concepts aren’t really ready for. 

ADAMS: I would agree with that. I think a few things to consider, vis-a-vis that, is that this revolution of patients using it is unstoppable. So it will happen. But we’re considering a project here at the National Academy about patients using AI and thinking about: let’s explore all the different facets of that. Let’s understand, what does safe usage look like? What might we do to help this new development enhance the patient-provider relationship and not erode it as we saw, “Don’t confuse your Google search with my medical degree” type of approach.  

Thinking about: how does it change the identity of the provider? How does it … what can we do to safely build a container in which patients can use this without giving them the sense that it’s being taken away, or that … because I just don’t see that happening. I don’t think they’re going to let it happen.  

That, to me, feels extremely important for us to explore all the dimensions of that. And that is one project that I hope to be following on to the AI Code of Conduct and applying the code of conduct principles with that project.  

LEE: Well, Laura, thank you again for joining us. And thank you even more for your tremendous national, even international, leadership on really helping mobilize the greatest institutions in a diverse way to fully confront the realities of AI in healthcare. I think it’s tremendously important work. 

ADAMS: Peter, thank you for having me. This has been an absolute pleasure. 

I’ve had the opportunity to watch Laura in action as she leads a national effort to define an AI code of conduct. And our conversation today has only heightened my admiration for her as a national leader.  

What impresses me is Laura’s recognition that technology adoption in healthcare has had a checkered history and furthermore oftentimes not accommodated the huge diversity of stakeholders that are affected equally. 

The concept of an AI code of conduct seems straightforward in some ways, but you can tell that every word in the emerging code has been chosen carefully. And Laura’s tireless engagement traveling to virtually every corner of the United States, as well as to several other countries, shows real dedication. 

And now here’s my conversation with Vardit Ravitsky:

LEE: Vardit, thank you so much for joining. 

RAVITSKY: It’s a real pleasure. I’m honored that you invited me. 

LEE: You know, we’ve been lucky. We’ve had a few chances to interact and work together within the National Academy of Medicine and so on. But I think for many of the normal subscribers to the Microsoft Research Podcast, they might not know what The Hastings Center for Bioethics is and then what you as the leader of The Hastings Center do every day. So I’d like to start there, first off, with what is The Hastings Center? 

RAVITSKY: Mostly, we’re a research center. We’ve been around for more than 55 years. And we’re considered one of the organizations that actually founded the field known today as bioethics, which is the field that explores the policy implications, the ethical, social issues in biomedicine. So we look at how biotechnology is rolled out; we look at issues of equity, of access to care. We look at issues at the end of life, the beginning of life, how our natural environment impacts our health. Any aspect of the delivery of healthcare, the design of the healthcare system, and biomedical research leading to all this. Any aspect that has an ethical implication is something that we’re happy to explore.  

We try to have broad conversations with many, many stakeholders, people from different disciplines, in order to come up with guidelines and recommendations that would actually help patients, families, communities.  

We also have an editorial department. We publish academic journals. We publish a blog. And we do a lot of public engagement activities—webinars, in-person events. So, you know, we just try to promote the thinking of the public and of experts on the ethical aspects of health and healthcare.  

LEE: One thing I’ve been impressed with, with your work and the work of The Hastings Center is it really confronts big questions but also gets into a lot of practical detail. And so we’ll get there. But before that just a little bit about you then. The way I like to ask this question is: how do you explain to your parents what you do every day? [LAUGHS] 

RAVITSKY: Funny that you brought my parents into this, Peter, because I come from a family of philosophers. Everybody in my family is in humanities, in academia. When I was 18, I thought that that was the only profession [LAUGHTER] and that I absolutely had to become a philosopher, or else what else can you do with your life? 

I think being a bioethicist is really about, on one hand, keeping an eye constantly on the science as it evolves. When a new scientific development occurs, you have to understand what’s happening so that you can translate that outside of science. So if we can now make a gamete from a skin cell so that babies will be created differently, you have to understand how that’s done, what that means, and how to talk about it.  

The second eye you keep on the ethics literature. What ethical frameworks, theories, principles have we developed over the last decades that are now relevant to this technology. So you’re really a bridge between science, biomedicine on one hand and humanities on the other hand.  

LEE: OK. So let’s shift to AI. And here I’d like to start with a kind of an origin story because I’m assuming before generative AI and ChatGPT became widely known and available, you must have had some contact with ideas in data science, in machine learning, and, you know, in the concept of AI before ChatGPT. Is that true? And, you know, what were some of those early encounters like for you? 

RAVITSKY: The earlier issues that I heard people talk about in the field were really around diagnostics and reading images and, Ooh, it looks like machines could perform better than radiologists. And, Oh, what if women preferred that their mammographies be read by these algorithms? And, Does that threaten us clinicians? Because it sort of highlights our limitations and weaknesses as, you know, the weakness of the human eye and the human brain.  

So there were early concerns about, will this outperform the human and potentially take away our jobs? Will it impact our authority with patients? What about de-skilling clinicians or radiologists or any type of diagnostician losing the ability … some abilities that they’ve had historically because machines take over? So those were the early-day reflections and interestingly some of them remain even now with generative AI.  

All those issues of the standing of a clinician, and what sets us apart, and will a machine ever be able to perform completely autonomously, and what about empathy, and what about relationships? Much of that translated later on into the, you know, more advanced technology. 

LEE: I find it interesting that you use words like our and we to implicitly refer to humans, homo sapiens, to human beings. And so do you see a fundamental distinction, a hard distinction that separates humans from machines? Or, you know, how … if there are replacements of some human capabilities or some things that human beings do by machines, you know, how do you think about that? 

RAVITSKY: Ooh, you’re really pushing hard on the philosopher in me here. [LAUGHTER] I’ve read books and heard lectures by those who think that the line is blurred, and I don’t buy that. I think there’s a clear line between human and machine.  

I think the issue of AGI—of artificial general intelligence—and will that amount to consciousness … again, it’s such a profound, deep philosophical challenge that I think it would take a lot of conceptual work to get there. So how do we define consciousness? How do we define morality? The way it stands now, I look into the future without being a technologist, without being an AI developer, and I think, maybe I hope, that the line will remain clear. That there’s something about humanity that is irreplaceable.  

But I’m also remembering that Immanuel Kant, the famous German philosopher, when he talked about what it means to be a part of the moral universe, what it means to be a moral agent, he talked about rationality and the ability to implement what he called the categorical imperative. And he said that would apply to any creature, not just humans. 

And that’s so interesting. It’s always fascinated me that so many centuries ago, he said such a progressive thing. 

LEE: That’s amazing, yeah. 

RAVITSKY: It is amazing because I often, as an ethicist, I don’t just ask myself, What makes us human? I ask myself, What makes us worthy of moral respect? What makes us holders of rights? What gives us special status in the universe that other creatures don’t have? And I know this has been challenged by people like Peter Singer who say [that] some animals should have the same respect. “And what about fetuses and what about people in a coma?” I know the landscape is very fraught.  

But the notion of what makes humans deserving of special moral treatment to me is the core question of ethics. And if we think that it’s some capacities that give us this respect, that make us hold that status, then maybe it goes beyond human. So it doesn’t mean that the machine is human, but maybe at [a] certain point, these machines will deserve a certain type of moral respect that … it’s hard for us right now to think of a machine as deserving that respect. That I can see. 

But completely collapsing the distinction between human and machine? I don’t think so, and I hope not. 

LEE: Yeah. Well, you know, in a way I think it’s easier to entertain this type of conversation post-ChatGPT. And so now, you know, what was your first personal encounter with what we now call generative AI, and what went through your mind as you had that first encounter? 

RAVITSKY: No one’s ever asked me this before, Peter. It almost feels exposing to share your first encounter. [LAUGHTER]  

So I just logged on, and I asked a simple question, but it was an ethical question. I framed an ethical dilemma because I thought, if I ask it to plan a trip, like all my friends already did, it’s less interesting to me.  

And within seconds, a pretty thoughtful, surprisingly nuanced analysis was kind of trickling down my screen, and I was shocked. I was really taken aback. I was almost sad because I think my whole life I was hoping that only humans can generate this kind of thinking using moral and ethical terms.  

And then I started tweaking my question, and I asked for specific philosophical approaches to this. And it just kept surprising me in how well it performed.  

So I literally had to catch my breath and, you know, sit down and go, OK, this is a new world, something very important and potentially scary is happening here. How is this going to impact my teaching? How is this going to impact my writing? How is this going to impact health? Like, it was really a moment of shock. 

LEE: I think the first time I had the privilege of meeting you, I heard you speak and share some of your initial framing of how, you know, how to think about the potential ethical implications of AI and the human impacts of AI in the future. Keeping in mind that people listening to this podcast will tend to be technologists and computer scientists as well as some medical educators and practicing clinicians, you know, what would you like them to know or understand most about your thoughts? 

RAVITSKY: I think from early on, Peter, I’ve been an advocate in favor of bioethics as a field positioning itself to be a facilitator of implementing AI. I think on one hand, if we remain the naysayers as we have been regarding other technologies, we will become irrelevant. Because it’s happening, it’s happening fast, we have to keep our eye on the ball, and not ask, “Should we do it?” But rather ask, “How should we do it?” 

And one of the reasons that bioethics is going to be such a critical player is that the stakes are so high. The risk of making a mistake in diagnostics is literally life and death; the risk of breaches of privacy that would lead to patients losing trust and refusing to use these tools; the risk of clinicians feeling overwhelmed and replaceable. The risks are just too high.  

And therefore, creating guardrails, creating frameworks with principles that sensitize us to the ethical aspects, that is critically important for AI and health to succeed. And I’m saying it as someone who wants it very badly to succeed. 

LEE: You are actually seeing a lot of healthcare organizations adopting and deploying AI. Has any aspect of that been surprising to you? Have you expected it to be happening faster or slower or unfolding in a different way? 

RAVITSKY: One thing that surprises me is how it seems to be isolated. Different systems, different institutions making their own, you know, decisions about what to acquire and how to implement. I’m not seeing consistency. And I’m not even seeing anybody at a higher level collecting all the information about who’s buying and implementing what under what types of principles and what are their outcomes? What are they seeing?  

It seems to be just siloed and happening everywhere. And I wish we collected all this data, even about how the decision is made at the executive level to buy a certain tool, to implement it, where, why, by whom. So that’s one thing that surprised me.  

The speed is not surprising me because it really solves problems that healthcare systems have been struggling with. What seems to be one of the more popular uses, and again, you know this better than I do, is the help with scribes with taking notes, ambient recording. This seems to be really desired because of burnout that clinicians face around this whole issue of note taking.  

And it’s also seen as a way to allow clinicians to do more human interaction, you know, … 

LEE: Right.  

RAVITSKY: … look at the patient, talk to the patient, … 

LEE: Yep. 

RAVITSKY: … listen, rather than focus on the screen. We’ve all sat across the desk with a doctor that never looks at us because they only look at the screen. So there’s a real problem here, and there’s a real solution and therefore it’s hitting the ground quickly.  

But what’s surprising to me is how many places don’t think that it’s their responsibility to inform patients that this is happening. So some places do; some places don’t. And to me, this is a fundamental ethical issue of patient autonomy and empowerment. And it’s also pragmatically the fear of a crisis of trust. People don’t like being recorded without their consent. Surprise, surprise.  

LEE: Mm-hmm. Yeah, yeah. 

RAVITSKY: People worry about such a recording of a very private conversation that they consider to be confidential, such a recording ending up in the wrong hands or being shared externally or going to a commercial entity. People care; patients care.  

So what is our ethical responsibility to tell them? And what is the institutional responsibility to implement these wonderful tools? I’m not against them, I’m totally in favor—implement these great tools in a way that respects long-standing ethical principles of informed consent, transparency, accountability for, you know, change in practice? And, you know, bottom line: patients right to know what’s happening in their care.  

LEE: You actually recently had a paper in a medical journal (opens in new tab) that touched on an aspect of this, which I think was not with scribes, but with notes, you know, … 

RAVITSKY: Yep. 

LEE: … that doctors would send to patients. And in fact, in previous episodes of this podcast, we actually talked to both the technology developers of that type of feature as well as doctors who were using that feature. And in fact, even in those previous conversations, there was the question, “Well, what does the patient need to know about how this note was put together?” So you and your coauthors had a very interesting recent paper about this. 

RAVITSKY: Yeah, so the trigger for the paper was that patients seemed to really like being able to send electronic messages to clinicians.  

LEE: Yes. 

RAVITSKY: We email and text all day long. Why not in health, right? People are used to communicating in that way. It’s efficient; it’s fast.   

So we asked ourselves, “Wait, what if an AI tool writes the response?” Because again, this is a huge burden on clinicians, and it’s a real issue of burnout.  

We surveyed hundreds of respondents, and basically what we discovered is that there was a statistically significant difference in their level of satisfaction when they got an answer from a human clinician, when they got an answer, again, electronic message from AI.  

And it turns out that they preferred the messages written by AI. They were longer, more detailed, even conveyed more empathy. You know, AI has all the time in the world [LAUGHS] to write you a text. It’s not rushing to the next one. 

But then when we disclosed who wrote the message, they were less satisfied when they were told it was AI.  

So the ethical question that that raises is the following: if your only metric is patient satisfaction, the solution is to respond using AI but not tell them that. 

Now when we compared telling them that it was AI or human or not telling them anything, their satisfaction remained high, which means that if they were not told anything, they probably assumed that it was a human clinician writing, because their satisfaction for human clinician or no disclosure was the same. 

So basically, if we say nothing and just send back an AI-generated response, they will be more satisfied because the response is nicer, but they won’t be put off by the fact that it was written by AI. And therefore, hey presto, optimal satisfaction. But we challenge that, and we say, it’s not just about satisfaction. 

It’s about long-term trust. It’s about your right to know. It’s about empowering you to make decisions about how you want to communicate.  

So we push back against this notion that we’re just there to optimize patient satisfaction, and we bring in broader ethical considerations that say, “No, patients need to know.” If it’s not the norm yet to get your message from AI, … 

LEE: Yeah. 

RAVITSKY: … they should know that this is happening. And I think, Peter, that maybe we’re in a transition period. 

It could be that in two years, maybe less than that, most of our communication will come back from AI, and we will just take it for granted …  

LEE: Right. 

RAVITSKY: … that that’s the case. And at that point, maybe disclosure is not necessary because many, many surveys will show us that patients assume that, and therefore they are informed. But at this point in time, when it’s transition and it’s not the norm yet, I firmly think that ethics requires that we inform patients. 

LEE: Let me push on this a little bit because I think this final point that you just made is, I think is so interesting. Does it matter what kind of information is coming from a human or AI? Is there a time when patients will have different expectations for different types of information from their doctors? 

RAVITSKY: I think, Peter, that you’re asking the right question because it’s more nuanced. And these are the kinds of empirical questions that we will be exploring in the coming months and years. Our recent paper showed that there was no difference regarding the content. If the message was about what we call the “serious” matter or a less “serious” matter, the preferences were the same. But we didn’t go deep enough into that. That would require a different type of design of study. And you just said, you know, there are different types of information. We need to categorize them.  

LEE: Yeah.  

RAVITSKY: What types of information and what degree of impact on your life? Is it a life-and-death piece of information? Is it a quality-of-life piece of information? How central is it to your care and to your thinking? So all of that would have to be mapped out so that we can design these studies.  

But you know, you pushed back in that way, and I want to push back in a different direction that to me is more fundamental and philosophical. How much do we know now? You know, I keep saying, oh, patients deserve a chance for informed consent, … 

LEE: Right.  

RAVITSKY: … and they need to be empowered to make decisions. And if they don’t want that tool used in their care, then they should be able to say, “No.” Really? Is that the world we live in now? [LAUGHTER] Do I have access to the black box that is my doctor’s brain? Do I know how they performed on this procedure in the last year? 

Do I know whether they’re tired? Do I know if they’re up to speed on the literature with this? We already deal with black boxes, except they’re not AI. And I think the evidence emerges that AI outperforms the humans in so many of these tasks.  

So my pushback is, are we seeing AI exceptionalism in the sense that if it’s AI, Huh, panic! We have to inform everybody about everything, and we have to give them choices, and they have to be able to reject that tool and the other tool versus, you know, the rate of human error in medicine is awful. People don’t know the numbers. The annual deaths attributed to medical error is outrageous.  

So why are we so focused on informed consent and empowerment regarding implementation of AI and less in other contexts. Is it just because it’s new? Is it because it is some sort of existential threat, … 

LEE: Yep, yeah. 

RAVITSKY: … not just a matter of risk? I don’t know the answer, but I don’t want us to suffer from AI exceptionalism, and I don’t want us to hold AI to such a high standard that we won’t be able to benefit from it. Whereas, again, we’re dealing with black boxes already in medicine. 

LEE: Just to stay on this topic, though, one more question, which is, maybe, almost silly in how hypothetical it is. If instead of email, it were a Teams call or a Zoom call, doctor-patient, except that the doctor is not the real doctor, but it’s a perfect replica of the doctor designed to basically fool the patient that this is the real human being and having that interaction. Does that change the bioethical considerations at all? 

RAVITSKY: I think it does because it’s always a question of, are we really misleading? Now if you get a text message in an environment that, you know, people know AI is already integrated to some extent, maybe not your grandmother, but the younger generation is aware of this implementation, then maybe you can say, “Hmm. It was implied. I didn’t mean to mislead the patient.” 

If the patient thinks they’re talking to a clinician, and they’re seeing, like—what if it’s not you now, Peter? What if I’m talking to an avatar [LAUGHS] or some representation of you? Would I feel that I was misled in recording this podcast? Yeah, I would. Because you really gave me good reasons to assume that it was you.  

So it’s something deeper about trust, I think. And it touches on the notion of empathy. A lot of literature is being developed now on the issue of: what will remain the purview of the human clinician? What are humans good for [LAUGHS] when AI is so successful and especially in medicine?  

And if we see that the text messages are read as conveying more empathy and more care and more attention, and if we then move to a visual representation, facial expressions that convey more empathy, we really need to take a hard look at what we mean by care. What about then the robots, right, that we can touch, that can hug us?  

I think we’re really pushing the frontier of what we mean by human interaction, human connectedness, care, and empathy. This will be a lot of material for philosophers to ask themselves the fundamental question you asked me at first: what does it mean to be human?  

But this time, what does it mean to be two humans together and to have a connection?  

And if we can really be replaced in the sense that patients will feel more satisfied, more heard, more cared for, do we have ethical grounds for resisting that? And if so, why?  

You’re really going deep here into the conceptual questions, but bioethics is already looking at that. 

LEE: Vardit, it’s just always amazing to talk to you. The incredible span of what you think about from those fundamental philosophical questions all the way to the actual nitty gritty, like, you know, what parts of an email from a doctor to a patient should be marked as AI. I think that span is just incredible and incredibly needed and useful today. So thank you for all that you do. 

RAVITSKY: Thank you so much for inviting me. 

The field of bioethics, and this is my take, is all about the adoption of disruptive new technologies into biomedical research and healthcare. And Vardit is able to explain this with such clarity. I think one of the reasons that AI has been challenging for many people is that its use spans the gamut from the nuts and bolts of how and when to disclose to patients that AI is being used to craft an email, all the way to, what does it mean to be a human being caring for another human?  

What I learned from the conversation with Vardit is that bioethicists are confronting head-on the issue of AI in medicine and not with an eye towards restricting it, but recognizing that the technology is real, it’s arrived, and needs to be harnessed now for maximum benefit.  

And so now, here’s my conversation with Dr. Roxana Daneshjou: 

LEE: Roxana, I’m just thrilled to have this chance to chat with you. 

ROXANA DANESHJOU: Thank you so much for having me on today. I’m looking forward to our conversation. 

LEE: In Microsoft Research, of course, you know, we think about healthcare and biomedicine a lot, but I think there’s less of an understanding from our audience what people actually do in their day-to-day work. And of course, you have such a broad background, both on the science side with a PhD and on the medical side. So what’s your typical work week like? 

DANESHJOU: I spend basically 90% of my time working on running my research lab (opens in new tab) and doing research on how AI interacts with medicine, how we can implement it to fix the pain points in medicine, and how we can do that in a fair and responsible way. And 10% of my time, I am in clinic. So I am a practicing dermatologist at Stanford, and I see patients half a day a week. 

LEE: And your background, it’s very interesting. There’s always been these MD-PhDs in the world, but somehow, especially right now with what’s happening in AI, people like you have become suddenly extremely important because it suddenly has become so important to be able to combine these two things. Did you have any inkling about that when you started, let’s say, on your PhD work? 

DANESHJOU: So I would say that during my—[LAUGHS] because I was in training for so long—during … my PhD was in computational genomics, and I still have a significant interest in precision medicine, and I think AI is going to be central to that.  

But I think the reason I became interested in AI initially is that I was thinking about how we associate genetic factors with patient phenotypes. Patient phenotypes being, How does the disease present? What does the disease look like? And I thought, you know, AI might be a good way to standardize phenotypes from images of, say, skin disease, because I was interested in dermatology at that time. And, you know, the part about phenotyping disease was a huge bottleneck because you would have humans sort of doing the phenotyping.  

And so in my head, when I was getting into the space, I was thinking I’ll bring together, you know, computer vision and genetics to try to, you know, make new discoveries about how genetics impacts human disease. And then when I actually started my postdoc to learn computer vision, I went down this very huge rabbit hole, which I am still, I guess, falling down, where I realized, you know, about biases in computer vision and how much work needed to be done for generalizability. 

And then after that, large language models came out, and, like, everybody else became incredibly interested in how this could help in healthcare and now also vision language models and multimodal models. So, you know, we’re just tumbling down the rabbit hole.  

LEE: Indeed, I think you really made a name for yourself by looking at the issues of biases, for example, in training datasets. And that was well before large language models were a thing. Maybe our audience would like to hear a little bit more about that earlier work.  

DANESHJOU: So as I mentioned, my PhD was in computational genetics. In genetics, what has happened during the genetic revolution is these large-scale studies to discover how genetic variation impacts human disease and human response to medication, so that’s what pharmacogenomics is, is human response to medications. And as I got, you know, entrenched in that world, I came to realize that I wasn’t really represented in the data. And it was because the majority of these genetic studies were on European ancestry individuals. You weren’t represented either. 

LEE: Right, yeah. 

DANESHJOU: Many diverse global populations were completely excluded from these studies, and genetic variation is quite diverse across the globe. And so you’re leaving out a large portion of genetic variation from these research studies. Now things have improved. It still needs work in genetics. But definitely there has been many amazing researchers, you know, sounding the alarm in that space. And so during my PhD, I actually focused on doing studies of pharmacogenomics in non-European ancestry populations. So when I came to computer vision and in particular dermatology, where there were a lot of papers being published about how AI models perform at diagnosing skin disease and several papers essentially saying, oh, it’s equivalent to a dermatologist—of course, that’s not completely true because it’s a very sort of contrived, you know, setting of diagnosis— … 

LEE: Right, right. 

DANESHJOU: … but my first inkling was, well, are these models going to be able to perform well across skin tones? And one of our, you know, landmark papers, which was in Science Advances (opens in new tab), showed … we created a diverse dataset, our own diverse benchmark of skin disease, and showed that the models performed significantly worse on brown and black skin. And I think the key here is we also showed that it was an addressable problem because when we fine-tuned on diverse skin tones, you could make that bias go away. So it was really, in this case, about what data was going into the training of these computer vision models. 

LEE: Yeah, and I think if you’re listening to this conversation, if you haven’t read that paper, I think it’s really must reading. It was not only, Roxana, it wasn’t only just a landmark scientifically and medically, but it also sort of crossed the chasm and really became a subject of public discourse and debate, as well. And I think you really changed the public discourse around AI.  

So now I want to get into generative AI. I always like to ask, what was your first encounter with generative AI personally? And what went through your head? You know, what was that experience like for you? 

DANESHJOU: Yeah, I mean, I actually tell this story a lot because I think it’s a fun story. So I would say that I had played with, you know, GPT-3 prior and wasn’t particularly, you know, impressed … 

LEE: Yeah. 

DANESHJOU: … by how it was doing. And I was at NeurIPS [Conference on Neural Information Processing Systems] in New Orleans, and I was … we were walking back from a dinner. I was with Andrew Beam from Harvard. I was with his group. 

And we were just, sort of, you know, walking along, enjoying the sites of New Orleans, chatting. And one of his students said, “Hey, OpenAI just released this thing called ChatGPT.”  

LEE: So this would be New Orleans in December … 

DANESHJOU: 2022.  

LEE: 2022, right? Yes. Uh-huh. OK. 

DANESHJOU: So I went back to my hotel room. I was very tired. But I, you know, went to the website to see, OK, like, what is this thing? And I started to ask it medical questions, and I started all of a sudden thinking, “Uh-oh, we have made … we have made a leap here; something has changed.”  

LEE: So it must have been very intense for you from then because months later, you had another incredibly impactful, or landmark, paper basically looking at biases, race-based medicine in large language models (opens in new tab). So can you say more about that? 

DANESHJOU: Yes. I work with a very diverse team, and we have thought about bias in medicine, not just with technology but also with humans. Humans have biases, too. And there’s this whole debate around, is the technology going to be more biased than the humans? How do we do that? But at the same time, like, the technology actually encodes the biases of humans.  

And there was a paper in the Proceedings of the National Academy of Sciences (opens in new tab), which did not look at technology at all but actually looked at the race-based biases of medical trainees that were untrue and harmful in that they perpetuated racist beliefs.  

And so we thought, if medical trainees and humans have these biases, why don’t we see if the models carry them forward? And we added a few more questions that we, sort of, brainstormed as a team, and we started asking the models those questions. And … 

LEE: And by this time, it was GPT-4?  

DANESHJOU: We did include GPT-4 because GPT-4 came out, as well. And we also included other models, as well, such as Claude, because we wanted to look across the board. And what we found is that all of the models had instances of perpetuating race-based medicine. And actually, the GPT models had one of the most, I think, one of the most egregious responses—and, again, this is 3.5 and 4; we haven’t, you know, fully checked to see what things look like, because there have been newer models—in that they said that we should use race in calculating kidney function because there were differences in muscle mass between the races. And this is sort of a racist trope in medicine that is not true because race is not based on biology; it’s a social construct.   

So, yeah, that was that study. And that one did spur a lot of public conversation. 

LEE: Your work there even had the issue of bias overtake hallucination, you know, as really the most central and most difficult issue. So how do you think about bias in LLMs, and does that in your mind disqualify the use of large language models from particular uses in medicine?  

DANESHJOU: Yeah, I think that the hallucinations are an issue, too. And in some senses, they might even go with one another, right. Like, if it’s hallucinating information that’s not true but also, like, biased.  

So I think these are issues that we have to address with the use of LLMs in healthcare. But at the same time, things are moving very fast in this space. I mean, we have a secure instance of several large language models within our healthcare system at Stanford so that you could actually put secure patient information in there.  

So while I acknowledge that bias and hallucinations are a huge issue, I also acknowledge that the healthcare system is quite broken and needs to be improved, needs to be streamlined. Physicians are burned out; patients are not getting access to care in the appropriate ways. And I have a really great story about that, which I can share with you later.  

So in 2024, we did a study asking dermatologists, are they using large language models (opens in new tab) in their clinical practice? And I think this percentage has probably gone up since then: 65% of dermatologists reported using large language models in their practices on tasks such as, you know, writing insurance authorization letters, you know, writing information sheets for the patient, even, sort of, using them to educate themselves, which makes me a little nervous because in my mind, the best use of large language models right now are cases where you can verify facts easily.  

So, for example, I did show and teach my nurse how to use our secure large language model in our healthcare system to write rebuttal letters to the insurance. I told her, “Hey, you put in these bullet points that you want to make, and you ask it to write the letter, and you can verify that the letter contains the facts which you want and which are true.” 

LEE: Yes. 

DANESHJOU: And we have also done a lot of work to try to stress test models because we want them to be better. And so we held this red-teaming event at Stanford where we brought together 80 physicians, computer scientists, engineers and had them write scenarios and real questions that they might ask on a day to day or tasks that they might actually ask AI to do. 

And then we had them grade the performance. And we did this with the GPT models. At the  time, we were doing it with GPT-3.5, 4, and 4 with internet. But before the paper (opens in new tab) came out, we then ran the dataset on newer models.  

And we made the dataset public (opens in new tab) because I’m a big believer in public data. So we made the dataset public so that others could use this dataset, and we labeled what the issues were in the responses, whether it was bias, hallucination, like, a privacy issue, those sort of things. 

LEE: If I think about the hits or misses in our book, you know, we actually wrote a little bit, not too much, about noticing biases. I think we underestimated the magnitude of the issue in our book. And another element that we wrote about in our book is that we noticed that the language model, if presented with some biased decision-making, more often than not was able to spot that the decision-making was possibly being influenced by some bias. What do you make of that?  

DANESHJOU: So funny enough, I think we had … we had a—before I moved from Twitter to Bluesky—but we had a little back and forth on Twitter about this, which actually inspired us to look into this as a research, and we have a preprint up on it of actually using other large language models to identify bias and then to write critiques that the original model can incorporate and improve its answer upon. 

I mean, we’re moving towards this sort of agentic systems framework rather than a singular large language model, and people, of course, are talking about also retrieval-augmented generation, where you sort of have this corpus of, you know, text that you trust and find trustworthy and have that incorporated into the response of the model.  

And so you could build systems essentially where you do have other models saying, “Hey, specifically look for bias.” And then it will sort of focus on that task. And you can even, you know, give it examples of what bias is within context learning now. So I do think that we are going to be improving in this space. And actually, my team is … most recently, we’re working on building patient-facing chatbots. That’s where my, like, patient story comes in. But we’re building patient-facing chatbots. And we’re using, you know, we’re using prompt-engineering tools. We’re using automated eval tools. We’re building all of these things to try to make it more accurate and less bias. So it’s not just like one LLM spitting out an answer. It’s a whole system. 

LEE: All right. So let’s get to your patient-facing story.  

DANESHJOU: Oh, of course. Over the summer, my 6-year-old fell off the monkey bars and broke her arm. And I picked her up from school. She’s crying so badly. And I just look at her, and I know that we’re in trouble. 

And I said, OK, you know, we’re going straight to the emergency room. And we went straight to the emergency room. She’s crying the whole time. I’m almost crying because it’s just, like, she doesn’t even want to go into the hospital. And so then my husband shows up, and we also had the baby, and the baby wasn’t allowed in the emergency room, so I had to step out. 

And thanks to the [21st Century] Cures Act (opens in new tab), I’m getting, like, all the information, you know, as it’s happening. Like, I’m getting the x-ray results, and I’m looking at it. And I can tell there’s a fracture, but I can’t, you know, tell, like, how bad it is. Like, is this something that’s going to need surgery?  

And I’m desperately texting, like, all the orthopedic folks I know, the pediatricians I know. [LAUGHTER] “Hey, what does this mean?” Like, getting real-time information. And later in the process, there was a mistake in her after-visit summary about how much Tylenol she could take. But I, as a physician, knew that this dose was a mistake.  

I actually asked ChatGPT. I gave it the whole after-visit summary, and I said, are there any mistakes here? And it clued in that the dose of the medication was wrong. So again, I—as a physician with all these resources—have difficulty kind of navigating the healthcare system; understanding what’s going on in x-ray results that are showing up on my phone; can personally identify medication dose mistakes, but you know, most people probably couldn’t. And it could be very … I actually, you know, emailed the team and let them know, to give feedback.  

So we have a healthcare system that is broken in so many ways, and it’s so difficult to navigate. So I get it. And so that’s been, you know, a big impetus for me to work in this space and try to make things better. 

LEE: That’s an incredible story. It’s also validating because, you know, one of the examples in our book was the use of an LLM to spot a medication error that a doctor or a nurse might make. You know, interestingly, we’re finding no sort of formalized use of AI right now in the field. But anecdotes like this are everywhere. So it’s very interesting.  

All right. So we’re starting to run short on time. So I want to ask you a few quick questions and a couple that might be a little provocative.  

DANESHJOU: Oh boy. [LAUGHTER] Well, I don’t run away … I don’t run away from controversy. 

LEE: So, of course, with that story you just told, I can see that you use AI yourself. When you are actually in clinic, when you are being a dermatologist … 

DANESHJOU: Yeah.  

LEE: … and seeing patients, are you using generative AI? 

DANESHJOU: So I do not use it in clinic except for the situation of the insurance authorization letters. And even, I was offered, you know, sort of an AI-based scribe, which many people are using. There have been some studies that show that they can make mistakes. I have a human scribe. To me, writing the notes is actually part of the thinking process. So when I write my notes at the end of the day, there have been times that I’ve all of a sudden had an epiphany, particularly on a complex case. But I have used it to write, you know, sort of these insurance authorization letters. I’ve also used it in grant writing. So as a scientist, I have used it a lot more.  

LEE: Right. So I don’t know of anyone who has a more nuanced and deeper understanding of the issues of biases in AI in medicine than you. Do you think [these] biases can be repaired in AI, and if not, what are the implications? 

DANESHJOU: So I think there are several things here, and I just want to be thoughtful about it. One, I think, the bias in the technology comes from bias in the system and bias in medicine, which very much exists and is incredibly problematic. And so I always tell people, like, it doesn’t matter if you have the most perfect, fair AI. If you have a biased human and you add those together, because you’re going to have this human-AI interaction, you’re still going to have a problem.  

And there is a paper that I’m on with Dr. Matt Groh (opens in new tab), which looked at looking at dermatology diagnosis across skin tones and then with, like, AI assistance. And we found there’s a bias gap, you know, with even physicians. So it’s not just an AI problem; humans have the problem, too. And… 

LEE: Hmm. Yup. 

DANESHJOU: … we also looked at when you have the human-AI system, how that impacts the gap because you want to see the gap close. And it was kind of a mixed result in the sense that there was actually situations where, like, the accuracy increased in both groups, but the gap actually also increased because they were actually, even though they knew it was a fair AI, for some reason, they were relying upon the AI more often when … or they were trusting it more often on diagnoses on white skin—maybe they’d read my papers, who knows? [LAUGHTER]—even though we had told them, you know, it was a fair model.  

So I think for me, the important thing is understanding how the AI model works with the physician at the task. And what I would like to see is it improve the overall bias and disparities with that unit.  

And at the same time, I tell human physicians, we have to work on ourselves. We have to work on our system, you know, our medical system that has systemic issues of access to care or how patients get treated based on what they might look like or other parts of their background.  

LEE: All right, final question. So we started off with your stories about imaging in dermatology. And of course, Geoff Hinton, Turing winner and one of the grandfathers of the AI revolution, famously had predicted many years ago that by 2018 or something like that, we wouldn’t need human radiologists because of AI. 

That hasn’t come to pass, but since you work in a field that also is very dependent on imaging technologies, do you see a future when radiologists or, for that matter, dermatologists might be largely replaced by machines? 

DANESHJOU: I think that’s a complex question. Let’s say you have the most perfect AI systems. I think there’s still a lot of nuance in how these, you know, things get done. I’m not a radiologist, so I don’t want to speak to what happens in radiology. But in dermatology, it ends up being quite complex, the process. 

LEE: Yeah. 

DANESHJOU: You know, I don’t just look at lesions and make diagnoses. Like, I do skin exams to first identify the lesions of concern. So maybe if we had total-body photography that could help, like, catch which lesions would be of concern, which people have worked on, that would be step, sort of, step one.  

And then the second thing is, you know, it’s … I have to do the biopsy. So, you know, the robot’s not going to be doing the biopsy. [LAUGHTER]  

And then the pathology for skin cancer is sometimes very clear, but there’s also, like, intermediate-type lesions where we have to make a decision bringing all that information together. For rashes, it can be quite complex. And then we have to kind of think about what other tests we’re going to order, what therapeutics we might try first, that sort of stuff.  

So, you know, there is a thought that you might have AI that could reason through all of those steps maybe, but I just don’t feel like we’re anywhere close to that at all. I think the other thing is AI does a lot better on sort of, you know, tasks that are well defined. And a lot of things in medicine, like, it would be hard to train the model on because it’s not well defined. Even human physicians would disagree on the next best step.  

LEE: Well, Roxana, for whatever it’s worth, I can’t even begin to imagine anything replacing you. I think your work has been just so—I think you used the word, and I agree with it— landmark, and multiple times. So thank you for all that you’re doing and thank you so much for joining this podcast.  

DANESHJOU: Thanks for having me. This was very fun. 

The issue of bias in AI has been the subject of truly landmark work by Roxana and her collaborators. And this includes biases in large language models.  

This was something that in our writing of the book, Carey, Zak, and I recognized and wrote about. But in fairness, I don’t think Carey, Zak, or I really understood the full implications of it. And this is where Roxana’s work has been so illuminating and important. 

Roxana’s practical prescriptions around red teaming have proven to be important in practice, and equally important were Roxana’s insights into how AI might always be guilty of the same biases, not only of individuals but also of whole healthcare organizations. But at the same time, AI might also be a potentially powerful tool to detect and help mitigate against such biases. 

When I think about the book that Carey, Zak, and I wrote, I think when we talked about laws, norms, ethics, regulations, it’s the place that we struggled the most. And in fact, we actually relied on a conversation with GPT-4 in order to tease out some of the core issues. Well, moving on from that conversation with an AI to a conversation with three deep experts who have dedicated their careers to making sure that we can harness all of the goodness while mitigating against the risks of AI, it’s been both fulfilling, very interesting, and a great learning experience. 

I’d like to say thank you again to Laura, Vardit, and Roxana for sharing their stories and insights. And to our listeners, thank you for joining us. We have some really great conversations planned for the coming episodes, including an examination on the broader economic impact of AI in health and a discussion on AI drug discovery. We hope you’ll continue to tune in.  

Until next time. 

Transcript

“In healthcare settings, keeping a human in the loop looks like the solution, at least for now, to GPT-4’s less-than 100% accuracy. But years of bitter experience with ‘Dr. Google’ and the COVID ‘misinfodemic’ show that it matters which humans are in the loop, and that leaving patients to their own electronic devices can be rife with pitfalls. Yet because GPT-4 appears to be such an extraordinary tool for mining humanity’s store of medical information, there’s no question members of the public will want to use it that way—a lot.” 

This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.  

Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?   

In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here. 

The passage I read at the top there is from Chapter 5, “The AI-Augmented Patient,” which Carey wrote.  

People have forever turned to the internet and sites like WebMD, Healthline, and so on to find health information and advice. So it wouldn’t be too surprising to witness a significant portion of people refocus those efforts around tools and apps powered by generative AI. Indeed, when we look at our search and advertising businesses here at Microsoft, we find that healthcare is in the top three most common categories of queries by consumers. 

When we envision AI’s potential impact on the patient experience, in our book, we suggested that it could potentially be a lifeline, especially for those without easy access to adequate healthcare; a research partner to help people make sense of existing providers and treatments; and even maybe act as a third member of a care team that has traditionally been defined by the doctor-patient relationship. This also could have a huge impact on venture capitalists in the tech sector who traditionally have focused on consumer-facing technologies.  

In this episode, I’m pleased to welcome Dave deBronkart and Christina Farr.  

Dave, known affectionately online as “e-Patient Dave,” is a world-leading advocate for empowering patients. Drawing on his experience as a survivor of stage 4 cancer, Dave gave a viral TED talk on patient engagement and wrote the highly rated book Let Patients Help! Dave was the Mayo Clinic’s visiting professor in internal medicine in 2015, has spoken at hundreds of conferences around the globe, and today runs the Patients Use AI blog on Substack. 

Chrissy puts her vast knowledge of the emerging digital and health technology landscape to use as a managing director with Manatt Health, a company that works with health systems, pharmaceutical and biotech companies, government policymakers, and other stakeholders to advise on strategy and technology adoption with the goal of improving human health. Previously, she was a health tech reporter and on-air contributor for CNBC, Fast Company, Reuters, and other renowned news organizations and publications. 

Hardly a week goes by without a news story about an ordinary person who managed to address their health problems—maybe even save their lives or the lives of their loved ones, including in some cases their pets—through the use of a generative AI system like ChatGPT. And if it’s not doing something as dramatic as getting a second opinion on a severe medical diagnosis, the empowerment that people feel when an AI can help decode an indecipherable medical bill or report or get advice on what to ask a doctor, well, those things are both meaningful and a daily reality in today’s AI world. 

And make no mistake—such consumer empowerment could mean business, really big business, and this means that investors in new ventures are smart to be taking a close look at all this.  

For these and many other reasons, I am thrilled to pair the perspectives offered by e-Patient Dave and Chrissy Farr together for this episode.

Here is my interview with Dave deBronkart: 

LEE: Dave, it’s just a thrill and honor to have you join us. 

DAVE DEBRONKART: It’s a thrill to be alive. I’m really glad that good medicine saved me, and it is just unbelievable, fun, and exciting and stimulating to be in a conversation with somebody like you. 

LEE: Likewise. Now, we’re going to want to get into both the opportunities and the challenges that patients face. But before that, I want to talk a little bit and delve a little bit more into you, yourself. I, of course, know you as this amazing speaker and advocate for patients. But you have had actually a pretty long career and history prior to all this. And so can you tell us a little bit about your background? 

DEBRONKART: I’ll go back all the way to when I first got out of college. I didn’t know what I wanted to do when I grew up. So I got a job where I … basically, I used my experience working on the school paper to get a temporary job. It was in type setting, if you can believe that. [LAUGHTER] And, man, a few years later, that became the ultimate lesson in disruptive innovation.  

LEE: So you were actually doing movable type? Setting type?  

DEBRONKART: Oh, no, that was, I was … I’m not that old, sir! [LAUGHTER] The first place where I worked, they did have an actual Linotype machine and all that.  

LEE: Wow. 

DEBRONKART: Anyway, one thing led to another. A few years after I got that first job, I was working for the world’s biggest maker of typesetting machines. And I did product marketing, and I learned how to speak to audiences of all different sorts. And then desktop publishing came along, as I say. And it’s so funny because, now mind you, this was 10 years before Clay Christensen wrote The Innovator’s Dilemma (opens in new tab). But I had already lived through that because here we were. We were the journeymen experts in our noble craft that had centuries of tradition as a background. Is this reminding you of anything? 

The clueless people gained experience, and they also started expressing what they wanted the software to be. The important thing is today everybody uses fonts. It’s no longer a secret profession. Things are done differently, but there is more power in the hands of the end user. 

LEE: Yeah, I think it’s so interesting to hear that story. I didn’t know that about your background. And I think it sheds some light on hopefully what will come out later as you have become such, I would call you a fierce consumer advocate. 

DEBRONKART: Sure, energetic, however, whatever you want to call it, sure. [LAUGHTER] Seriously, Peter, what I always look to do … so this is a mixture of my having been run over by a truck during disruptive innovation, all right, but then also looking at that experience from a marketing perspective: how can I convey what’s happening in a way that people can hear? Because you really don’t get much traction as an advocate if you come in and say, you people are messed up.  

LEE: Right. So, now I know this gets into something fairly personal, but you’ve actually been remarkably public about this. You became very ill.  

DEBRONKART: Yes.  

LEE: And of course, I suspect some of the listeners to this podcast probably have followed your story, but many have not. So can we go a little bit through that … 

DEBRONKART: Sure.  

LEE: … just to give our listeners a sense of how this has formed some of your views about the healthcare system. 

DEBRONKART: So late in 2006, I went in for my annual physical with my deservedly famous primary care physician, Danny Sands at Beth Israel [Deaconess Medical Center] in Boston. And in the process—I had moved away for a few years, so I hadn’t seen him for a while—I did something unusual. I came into the visit with a preprinted letter with 13 items I wanted to go over with him.   

LEE: What made you do that? Why did you do that? 

DEBRONKART: I have always been, even before I knew the term exists, I was an engaged patient, and I also very deeply believe in partnership with my physicians. And I respected his time. I had all these things, because I hadn’t seen him for three years … 

LEE: Yeah. 

DEBRONKART: … all these things I wanted to go through. To me it was just if I walked into a business meeting with a bunch of people that I hadn’t seen for three years and I want to get caught up, I’d have an agenda. 

LEE: It’s so interesting to hear you say this because I’m very similar to you. I like to do my own research. I like to come in with checklists. And do you ever get a sense like I do that sometimes that makes your doctor a little uncomfortable? 

DEBRONKART: [LAUGHS] Well, you know, so sometimes it does make some doctors uncomfortable and that touches on something that right now is excruciatingly important in the culture change that’s going on. I’ve spent a lot of time as I worked on the culture change from the patient side, I want to empathize, understand what’s going on in the doctor’s head. Most doctors are not trained in medical school or later, how do you work with a patient who behaves like you or me, you know?  

And in the hundreds of speeches that I’ve given, I’ve had quite a range of reactions from doctors afterwards. I’ve had doctors come up to me and say, “This is crap.” I mean, right to my face, right. “I’ll make the decisions. I’ll decide what we’re going to talk about.” And now my thought is, OK, and you’re not going to be my doctor. 

LEE: Yeah. 

DEBRONKART: I want to be responsible for how the time is spent, and I didn’t want be fumbling for words during the visit. 

LEE: Right. 

DEBRONKART: So I said, I’ve got among other things … one of the 13 things was I had a stiff shoulder. So he ordered a shoulder x-ray, and I went and got the shoulder x-ray.  

And I will never forget this. Nine o’clock the next morning, he called me, and I can still—this is burned into my memory—I can see the Sony desk phone with 0900 for the time. He said, “Dave, your shoulder’s going to be fine. I pulled up the x-ray on my screen at home. It’s just a rotator cuff thing, but Dave, something else showed up. There’s something in your lung that shouldn’t be there.”  

And just by total luck, what turned out to be a metastasis of kidney cancer was in my lung next to that shoulder. He immediately ordered a CAT scan. Turned out there were five tumors in both lungs, and I had stage 4 kidney cancer.  

LEE: Wow.  

DEBRONKART: And on top of that, back then—so this was like January of 2007—back then, there was much less known about that disease than there is now.  

LEE: Right. 

DEBRONKART: There were no studies—zero research on people like me—but the best available study said that for somebody with my functional status, my median survival was 24 weeks. Half the people like me would be dead in five and a half months. 

LEE: So that just, you know, I can’t imagine, you know, how I would react in this situation. And what were your memories of the interaction then between you and your doctor? You know, how did your doctor engage with you at that time? 

DEBRONKART: I have very vivid memories. [LAUGHS] Who was it? I can’t remember what famous person said, “Nothing focuses the mind like the knowledge that one is to be hanged in a fortnight,” right. But 24 weeks does a pretty good job of it.  

And I … just at the end of that phone call where he said I’m going to order a CAT scan, I said, “Is there anything I should do?” Like I was thinking, like, go home and make sure you don’t eat this sort of this, this, that, or the other thing.  

LEE: Right. 

DEBRONKART: And what he said was, “Go home and have a glass of wine with your wife.” 

LEE: Yeah. 

DEBRONKART: Boy, was that sobering. But then it’s like, all right, game on. What are we going to do? What are my options? And a really important thing, and this, by the way, this is one reason why I think there ought to be a special department of hell for the people who run hospitals and other organizations where they think all doctors are interchangeable parts. All right. My doctor knew me. 

LEE: Yeah. 

DEBRONKART: And he knew what was important to me. So when the biopsy came back and said, “All right, this is definitely stage 4, grade 4 renal cell carcinoma.” He knew me enough … he said, “Dave, you’re an online kind of guy. You might like to join this patient community that I know of.” This was 2007.  

LEE: Yeah. 

DEBRONKART: It’s a good quality group. This organization that barely exists. 

LEE: That’s incredibly progressive, technologically progressive for that time. 

DEBRONKART: Yeah, incredibly progressive. Now, a very important part of the story is this patient community is just a plain old ASCII listserv. You couldn’t even do boldface, right. And this was when the web was … web 2.0 was just barely being created, but what it was, was a community of people who saw the problems the way I see the problems. God bless the doctors who know all the medical stuff, you know. And they know the pathology and the morphology and whatever it is they all know.  

And I’m making a point here of illustrating that I am anything but medically trained, right. And yet I still, I want to understand as much as I can.  

I was months away from dead when I was diagnosed, but in the patient community, I learned that they had a whole bunch of information that didn’t exist in the medical literature. 

Now today we understand there’s publication delays; there’s all kinds of reasons. But there’s also a whole bunch of things, especially in an unusual condition, that will never rise to the level of deserving NIH [National Institute of Health] funding, right … 

LEE: Yes. 

DEBRONKART: … and research. And as it happens, because of the experience in that patient community, they had firsthand experience at how to survive the often-lethal side effects of the drug that I got. And so I talked with them at length and during my treatment, while I was hospitalized, got feedback from them. And several years later my oncologist, David McDermott, said in the BMJ [British Medical Journal], he said, “You were really sick. I don’t know if you could have tolerated enough medicine if you hadn’t been so prepared.” 

Now there is a case for action, for being actively involved, and pointing towards AI now, doing what I could to learn what I could despite my lack of medical education. 

LEE: But as you were learning from this patient community these things, there had to be times when that came into conflict with the treatment plan that you’re under. That must have happened. So first off, did it? And how were those conflicts resolved? 

DEBRONKART: So, yes, it did occasionally because in any large population of people you’re going to have differences of opinion. Now, before I took any action—and this closely matches the current thought of human in the loop, right—before I took any action based on the patient community, I checked with my clinicians.  

LEE: Were there times when there were things that … advice you were getting from the patient community that you were very committed to, personally, but your official, formal caregivers disagreed with? 

DEBRONKART: No, I can’t think of a single case like that. Now, let me be clear. My priority was: save my ass, keep me alive, you know? And if I thought a stranger at the other end of an internet pipe had a different opinion from the geniuses at my hospital—who the whole patient community had said, this is maybe the best place in the world for your disease— 

LEE: Yes. 

DEBRONKART: I was not going to go off and have some philosophical debate about epistemology and all of that stuff. And remember, the clock was ticking. 

LEE: Well, in fact, there’s a reason why I keep pressing on this point. It’s a point of curiosity because in the early days of GPT-4, there was an episode that my colleague and friend Greg Moore, who’s a neuroradiologist, had with a friend of his that became very ill with cancer.  

And she went in for treatment and the treatment plan was a specific course of chemotherapy, but she disagreed with that. She wanted a different type of, more experimental immunotherapy. And that disagreement became intractable to the point that the cancer specialists that were assigned to treat her asked Greg, “Can you talk to her and explain, you know, why we think our decision is best?”  

And the thing that was remarkable is Greg decided to use that case as one of the tests in the early development days of GPT-4 and had a conversation to explain the situation. They went back and forth. GPT-4 gave some very useful advice to Greg on what to say and how to frame it.  

And then, when Greg finally said, “You know, thank you for the help.” What floored both me and Greg is GPT-4 said, “You’re welcome. But, Greg, what about you? Are you getting all the support that you need? Here are some resources.”  

And, you know, I think we can kind of take that kind of behavior for granted today, and there have been some published studies about the seeming empathy of generative AI. 

But in those early days, it was eerie, it was awe-inspiring, it was disturbing—you know, all of these things at once. And that’s essentially why I’m so curious about your experiences along these lines. 

DEBRONKART: That’s like, that’s the flip side of the famous New York Times reporter who got into a late-night discussion …  

LEE: Oh, Kevin Roose, yes. [LAUGHTER] 

DEBRONKART: You say you’re happy in your marriage, but I think you’re not.  

LEE: Right. 

DEBRONKART: It’s like, whoa, this is creepy. But you know, it’s funny because one of the things that’s always intrigued me, partly because of my professional experience at explaining technology to people, is the early messaging around LLMs [large language models], which I still hear people … The people who say, “Well, wait a minute, these things hallucinate, so don’t trust them.” Or they say, “Look, all it’s doing is predicting the next word.”  

But there are loads of nuances, … 

LEE: Yes.  

DEBRONKART: … and that’s, I mean, it takes an extraordinary amount of empathy, not just for the other person’s feelings, but for their thought process … 

LEE: Hmm, yes. Yeah. 

DEBRONKART: … to be able to express that. Honestly, that is why I’m so excited about the arriving future. One immensely important thing … as I said earlier, I really respect my doctors’ time—“doctors” plural—and it breaks my heart that the doctors who did all this work to get license and all that stuff are quitting the field because the economic pressures are so great. I can go home and spend as many hours as I want asking it questions. 

LEE: Yes.  

DEBRONKART: All right. I’ve recently learned a thing to do after I have one of these hours-long sessions, I’ll say to it, “All right, so if I wanted to do this in a single-shot prompt, how would you summarize this whole conversation?” So having explored with no map, I end up with a perspective that it just helps me see the whole thing … 

LEE: Yes. Yeah, that’s brilliant. 

DEBRONKART: … without spending a moment of the doctor’s time.

LEE: Yeah, yeah. So when was the first time that you used, you know, generative AI?

DEBRONKART: It had to be February or March of whatever the first year was.  

LEE: Yeah. And was it the New York Times article that piqued your interest?  

DEBRONKART: Oh absolutely. 

LEE: Yeah. And so what did you think? Were you skeptical? Were you amazed? What went through your mind? 

DEBRONKART: Oh, no, no, no. It blew my mind. And I say that as somebody who emerged from the 1960s and ’70s, one of the original people who knew what it was to have your mind blown back in the psychedelic era. [LAUGHTER] No, it blew my mind. And it wasn’t just the things it said; it was the implications of the fact that it could do that.  

I did my first programming with BASIC or Fortran. I don’t know, something in the mid-’60s, when I was still in high school. So I understand, well, you know, you got to tell it exactly what you want it to do or it’ll do the wrong thing. So, yeah, for this to be doing something indistinguishable from thinking—indistinguishable from thinking—was completely amazing. And that immediately led me to start thinking about what this would mean in the hands of a sick person. And, you know, my particular area of fascination in medicine—everything I use it for these days is mundane—but the future of a new world of medicine and healthcare is one where I can explore and not be limited to things where you can read existing answers online. 

LEE: Right. So if you had GPT-4 back in 2006, 2007, when you were first diagnosed with your renal cancer, how would things have been different for you? Would things have been different for you? 

DEBRONKART: Oh, boy, oh, boy, oh, boy. This is going to have to be just a swag because, I mean, for it to—you mean, if it had just dropped out of thin air?  

LEE: Yes. [LAUGHS] 

DEBRONKART: Ah, well, that’s … that’s even weirder. First thing we in the patient community would have to do is figure out what this thing does … 

LEE: Yeah. 

DEBRONKART: … before we can start asking it questions.  

Now, Peter, a large part of my evangelism, you know, there’s a reason why my book (opens in new tab) and my TED talk (opens in new tab) were titled “Let Patients Help.” 

I really am interested in planting a thought in people’s minds, and it’s not covert. I come right out and say it in the title of the book, right, planting a thought that, with the passage of time, will hold up as a reasonable thing to do. And same thing is true with AI. So … and I’ve been thinking about it that way from the very beginning. I never closed the loop on my cancer story. I was diagnosed in January, and I had my last drop of high-dose interleukin—experimental immunotherapy, right—in July. And that was it. By September, they said, looks like you beat it. And I was all done.  

And there’s the question: how could it be that I didn’t die? How could it be that valuable information could exist and not be in the minds of most doctors? Not be in the pages of journals?  

And if you think of it that way, along the way, I became a fan of Thomas Kuhn’s famous book, The Structure of Scientific Revolutions (opens in new tab).  

LEE: Yes. 

DEBRONKART: When something that the paradigm says could not happen does happen, then responsible thinkers have to say, the paradigm must be wrong. That’s the stage of science that he called a crisis. So if something came along back in 2006, 2007, I would have to look at it and say, “This means we’ve got to rethink our assumptions.” 

LEE: Yes. You know, now with the passage of time, you know, over the last two years, we’ve seen so many stories like this, you know, where people have consulted AI for a second opinion, … 

DEBRONKART: Sure. 

LEE: … maybe uploaded their labs and so on and gotten a different diagnosis, a different treatment suggestion. And in several cases that have been reported, both in medical journals and in the popular press, it’s saved, it has saved lives. And then your point about communities, during COVID pandemic, even doctors form communities to share information. A very famous example are doctors turning to Facebook and Twitter to share that if they had a COVID patient in severe respiratory distress, sometimes they could avoid intubation by …  

DEBRONKART: Pronation. Yeah. 

LEE: … pronation. And things like this end up being, in a way, I think the way you’re couching it, ways to work around the restrictions in the more formal healthcare system. 

DEBRONKART: The traditional flow. Yes. And there is nothing like a forest fire, an emergency, an unprecedented threat to make people drop the usual formal pathways. 

LEE: So, I’d like to see if we can impart from your wisdom and experience some advice for specific stakeholders. So, what do you say to a patient? What do you say to a doctor? What do you say to the executive in charge of a healthcare system? And then finally, what do you say to policymakers and regulators? So, let’s start with patients. 

DEBRONKART: So if you’ve got a problem that or a question where you really want to understand more than you’ve been able to, then give a try to these things. Ask some questions. And it’s not just the individual question and answer. The famous, amazing patient advocate, Hugo Campos, … 

LEE: Hmm, yes. 

DEBRONKART: … said something that I call “Hugo’s Law.” He said, “Dave, I don’t ask it for answers. I use it to help me think.” 

LEE: Yes, absolutely.  

DEBRONKART: So you get an answer and you say, “Well, I don’t understand this. What about that? Well, what if I did something different instead?” And never forget, you can come back three months later and say, “By the way, I just thought of something. What about that,” right.  

LEE: Yeah, yeah, fantastic. 

DEBRONKART: So be focused on what you want to understand.  

LEE: So now let’s go to a doctor or a nurse. What’s the advice there?  

DEBRONKART: Please try to imagine a world … I know that most people today are not as activated as I am in wanting to be engaged in their health. But to a very large extent, people, a lot of people, family and friends, have said they don’t want to do this because they don’t want to offend the doctors and nurses. Now, even if the doctor or nurse is not being a paternal jerk, all right, the patients have a fear of this. Dr. Sands handles this brilliantly. I mentioned it in the book. He proactively asks, are there any websites you’ve found useful?  

And you can do the same thing with AI. Have you done anything useful with ChatGPT or something like that?  

LEE: That actually suggests some curricular changes in medical schools in order to train doctors.  

DEBRONKART: Absolutely. In November, I attended a retreat on rethinking medical education. I couldn’t believe it, Peter. They were talking about how AI can be used in doing medical education. And I was there saying, “Well, hello. As long as we’re here, let’s rethink how you teach doctors, medical students to deal with somebody like me.” Cause what we do not want …  

There was just a study in Israel where it said 18% of adults use AI regularly for medical questions, which matches other studies in the US.  

LEE: Yep.  

DEBRONKART: But it’s 25% for people under 25. We do not want 10 years from now to be minting another crop of doctors who tells patients to stay off of the internet and AI.  

LEE: You know, it’s such an important point. Students, you know, entering into college to go on to medical school and then a residency and then finally into practice. I think you’re thinking about the year 2035 or thereabouts. And when you think of that, at least in tech industry terms, we’re going to be on Mars, we’re going to have flying cars, we’re going to have AGI [artificial general intelligence], and you really do need to think ahead. 

DEBRONKART: Well, you know, healthcare, and this speaks to the problems that health system executives are facing: y’all better watch out or you’re going to be increasingly irrelevant, all right.  

One of the key use cases, and I’m not kidding … I mean, I don’t mean that if I have stage 4 kidney cancer, I’m going to go have a talk with my robot. But one of the key use cases that makes people sit down and try to solve a problem on their own with an LLM is if they can’t get an appointment.  

LEE: Yes. 

DEBRONKART: Well, so let’s figure out, can the health system, can physicians and patients learn to work together in some modified way? Nobody I know wants to stop seeing a doctor, but they do need to have their problems solved.  

LEE: Yeah, yeah. 

DEBRONKART: And there is one vitally important thing I want to … I insist that we get into this, Peter. In order for the AI to perform to the best of its contribution, it needs to know all the data. 

LEE: Yes.  

DEBRONKART: Well, and so does the patient. Another super-patient, James Cummings, has two rare-genetic-mutation kids. (opens in new tab) He goes to four Epic-using hospitals. Those doctors can’t see each other’s data. So he compiles it, and he shows … the patient brings in the consolidated data. 

LEE: Yes. Well, and I know this is something that you’ve really been passionate about, and you’ve really testified before Congress on. But maybe then that leads to this fourth category of people who need advice, which are policymakers and regulators. What would you tell them? 

DEBRONKART: It’s funny, in our current political environment, there’s lots of debates about regulation, more regulation, less regulation. I’m heavily in favor of the regulations that say, yeah, I gotta be able to see and download my damn data, as I’m famous for calling it. But what we need to do if we were to have any more regulations is just mandate that you can’t keep the data away from people who need it. You can’t when … 

LEE: Yep. 

DEBRONKART: OK, consider one of the most famous AI-using patients is this incredible woman, Courtney Hofmann, whose son saw 17 doctors over three years (opens in new tab), and she finally sat down one night and typed it all into GPT. She has created a startup to try to automate the process of gathering everyone’s data.  

LEE: Yes, yes. Yeah. 

DEBRONKART: And I know people who have been trying to do this and it’s just really hard. Policy people should say, look, I mean, we know that American healthcare is unsustainable economically. 

LEE: Yes. 

DEBRONKART: And one way to take the pressure off the system—because it ain’t the doctors’ fault, because they’re burned out and quitting—one way to take the pressure off is to put more data in the hands of the patients so that entrepreneurs can make better tools. 

LEE: Yeah. All right. So, we’ve run out of time, but I want to ask one last provocative question to send us off. Just based on your life’s experience, which I think is just incredible and also your personal generosity in sharing your stories with such a wide audience, I think is incredible. It’s just doing so much good in the world. Do you see a future where AI effectively replaces human doctors? Do you think that’s a world that we’re heading towards? 

DEBRONKART: No, no, no, no. People are always asking me this. I do imagine an increasing base, an increasing if … maybe there’s some Venn diagram or something, where the number of things that I can resolve on my own will increase.  

LEE: Mm-hmm. Yes. 

DEBRONKART: And in particular, as the systems get more useful, and as I gain more savvy at using them and so on, there will be cases where I can get it resolved good enough before I can get an appointment, right. But I cannot imagine a world without human clinicians. Now, I don’t know what that’s going to look like, right.

LEE: Yes. [LAUGHS]

DEBRONKART: I mean, who knows what it’s going to be. But I keep having … Hugo blogged this incredible vision of where his agentic AI will be looking at one of these consolidated blob medical records things, and so will his doctor’s agentic AI. 

LEE: Yes. Well, I think I totally agree with you. I think there’ll always be a need and a desire for the human connection. Dave, this has been an incredible, really at times, riveting conversation. And as I said before, thank you for being so generous with your personal stories and with all the activism and advocacy that you do for patients. 

DEBRONKART: Well, thank you. I’m, as I said at the beginning, I’m glad to be alive and I’m really, really, really grateful to be given a chance to share my thoughts with your audience because I really like super smart nerds.  
 
[LAUGHTER] No, well, no kidding. In preparing for this, I listened to a bunch of back podcast episodes, “Microsoft Research,” “NEJM AI.” They talk about things I do not comprehend and don’t get me started on quantum, right? [LAUGHTER] But I’m grateful and I hope I can contribute some guidance on how to solve the problem of the person for whom the industry exists. 

LEE: Yeah, you absolutely have done that. So thank you. 

E-Patient Dave is so much fun to talk to. His words and stories are dead serious, including his openness about his struggles with cancer. But he just has a way of engaging with the world with such activism and positivity. The conversation left me at least with a lot of optimism about what AI will mean for the consumer.  

One of the key takeaways for me is Dave’s point that sometimes informal patient groups have more up-to-date knowledge than doctors. One wonders whether AI will make these sorts of communities even more effective in the near future. It sure looks like it.  

And as I listen to Dave’s personal story about his bout with cancer, it’s a reminder that it can be lifesaving to do your own research, but ideally to do so in a way that also makes it possible to work with your caregivers. Healthcare, after all, is fundamentally a collaborative activity today. 

Now, here’s my conversation with Christina Farr: 

LEE: Chrissy, welcome. I’m just thrilled that you’ve joined us here. 

CHRISTINA FARR: Peter, I’m so excited to be here. Thanks for having me on. 

LEE: One thing that our listeners should know is you have a blog called Second Opinion (opens in new tab). And it’s something that I read religiously. And one of the things you wrote (opens in new tab) a while ago expressed some questions about as an investor or as a founder of a digital health company, if you don’t use the words AI prominently, you will struggle to gain investment. And you were raising some questions about this. So maybe we start there. And, you know, what are you seeing right now in the kind of landscape of emerging digital health tech companies? What has been both the positive and negative impact of the AI craziness that we have in the world today on that? 

FARR: Yeah, I think the title of that was something around the great AI capital incineration [LAUGHTER] that we were about to see. But I, you know, stand by it. I do think that we’ve sort of gone really deep into this hype curve with AI, and you see these companies really just sucking up the lion’s share of venture capital investment. 

And what worries me is that these are, you know, it’s really hard, and we know this from just like decades of being in the space that tools are very hard to monetize in healthcare. Most of healthcare still today and where really the revenue is, is in, still in services. It’s still in those kind of one-to-one interactions. And what concerns me is that we are investing in a lot of these AI tools that, you know, are intended to sell into the system. But the system doesn’t yet know how to buy them and then, beyond that, how to really integrate them into the workflow.  

So where I feel more enthusiastic, and this is a little bit against the grain of what a lot of VCs [venture capitalists] think, but I actually really like care delivery businesses that are fully virtual or hybrid and really using AI as part of their stack. And I think that improves really the style of medicine that they’re delivering and makes it far more efficient. And you start to see, you know, a real improvement in the metrics, like the gross margins of these businesses beyond what you would see in really traditional kind of care delivery. And because they are the ones that own the stack, they’re the ones delivering the actual care, … 

LEE: Right. 

FARR: … they can make the decision to incorporate AI, and they can bring in the teams to do that. And I feel like in the next couple of years, we’re going to see more success with that strategy than just kind of more tools that the industry doesn’t know what to do with. 

LEE: You know, I think one thing that I think I kind of learned or I think I had an inkling of it, but it was really reinforced reading your writings, as a techie, I and I think my colleagues tend to be predisposed to looking for silver bullets. You know, technology that really just solves a problem completely.  

And I think in healthcare delivery in particular, there probably aren’t silver bullets. And what you need to do is to really look holistically at things and your emphasis on looking for those metrics that measure those end-to-end outcomes. So at the same time, if I could still focus on your blog, you do highlight companies that seem to be succeeding that way.  

Just, in preparation for this discussion, I re-read your post about Flo (opens in new tab) being the first kind of unicorn women’s health digital tech startup. And there is actually a lot of very interesting AI technology involved there. So it can happen. How do you think about that? 

FARR: Yeah, I mean, I see a lot of AI across the board. And it’s real with some of these companies, whether it’s, you know, a consumer health app like Flo that, you know, is really focused on kind of period tracking. And AI is very useful there in helping women just predict things like their optimal fertility windows. And it’s very much kind of integrated very deeply into that solution. And they have really sophisticated technology.  

And you see that now as well with the kind of craze around these longevity companies, that there is a lot of AI kind of underlying these companies, as well, especially as they’re doing, you know, a lot of health tests and pulling in new data and providing access to that data in a way that, you know, historically patients haven’t had access to.  

And then I also see it with, you know, like I spoke about with these care delivery companies. I recently spent some time with a business called Origin (opens in new tab), for instance, which is in, you know, really in kind of women’s health, MSK [musculoskeletal], and that beachhead is in pelvic floor PT [physical therapy].  

And for them, you know, it’s useful in the back office for … a lot of their PT providers are getting great education through AI. And then it’s also useful on the patient-facing side as they provide kind of more and more content for you to do exercises at home. A lot of that can be delivered through AI. So for some of these companies, you know, they look across the whole stack of what they’re providing, and they’re just seeing opportunities in so many different places for AI. And I think that’s really exciting, and it’s very, very real. And it’s really to me like where I’m seeing kind of the first set of really kind of promising AI applications. There are definitely some really compelling AI tools, as well. 

I think companies like Nuance and like Abridge and that whole category of really kind of replacing human scribes with AI, like to me, that is a … that has been so successful because it literally is the pain point. It’s the pain point. You’re solving the pain point for health systems and physicians.  

Burnout is a huge problem. Documentation is a huge problem. So, you know, to say we’ve got this kind of AI solution, everybody’s basically on board—you know, as long as it works—[LAUGHTER] from the first meeting. And then the question becomes, which one do you choose? You know, that said, you know, to me, that’s sort of a standout area. I’m not seeing that everywhere.

LEE: So there are like a bunch of things to delve into there. You know, since you mentioned the Nuance, the Dragon Copilot, and Abridge, and they are doing extremely well. But even for them, and this is another thing that you write about extensively, health systems have a hard time justifying investing in these technologies. It’s not like they’re swimming in cash. And so on that element of things, is there advice to companies that are trying to make technologies to sell into health systems? 

FARR: Yeah, I mean, I’ll give you something really practical on that just example specifically. So I spend a lot of time chatting with a lot of the health system CMIOs [chief medical informatics officers] trying to, you know, just really understand kind of their take. And they often tell me, “Look, you know, these technologies are not inexpensive, and we’ve already spent a boatload of money on REHR [regional electronic health records], which continues to be expensive. And so we just don’t have a lot of budget.” And for them, I think the question becomes, you know, who within the clinical organization would benefit most from these tools?  

There are going to be progressive physicians that will jump on these on day one and start using them and really integrating them into the workflow. And there will be a subset that just wants to do things the way they always have done things. And you don’t want to pay for seats for everybody when there’s a portion that will not be using it. So I think that’s maybe something that I would kind of share with the startup crowd is just, like, don’t try to sell to every clinician within the organization. Not everybody is going to be, you know, a technology early adopter. Work with the health systems to figure out that cohort that’s likely to jump on board first and then kind of go from there. 

LEE: So now let me get back to specifically to women’s health. I think your investing strategy has, I think it’s fair to say has had some emphasis on women’s health. And I would say for me, that has always made sense because if there’s one thing the tech industry knows how to do in any direct-to-consumer business is to turn engagement into dollars.  

And when you think about healthcare, there are very few moments in a person’s life when they have a lot of engagement with their own healthcare. But women have many. You mentioned period tracking, pregnancy, menopause. There are so many areas where you could imagine that technology could be good. At least that’s way I would think about it, but does that make any sense to you, or do you have a different thought process?  

FARR: Oh, my god, I’ve been, I’m just nodding right now because I’ve been saying the same thing for years, [LAUGHS] that like, I think the, you know, the moments of what I call naturally high engagement are most interesting to me. And I think it’s why it’s been such a struggle with some of these companies that are looking at, you know, areas like or conditions like type two diabetes.  

I mean, it’s just so hard to try to change somebody’s behavior, especially through technology. You know, we’ve not kind of proven out that these nudges are really changing anybody’s mind about, you know, their day-to-day lifestyles. Whereas, you know, in these moments, like you said, of just like naturally high engagement … like it’s, you know, women’s health, you’re right, there’s a lot of them. Like if you’re pregnant, you’re very engaged. If you’re going through menopause, you’re very engaged. And I think there are other examples like this, you know, such as oncology. You get a cancer diagnosis, you’re very engaged. 

And so, to me, that’s really kind of where I see the most interesting opportunities for technology and for digital health.  

And, you know, one example I’ll give you in women’s health, I’m not invested in this company, sadly. They are called Midi Health (opens in new tab). And they’re really everywhere in the menopause area now, like, you know, the visit volume that they are seeing is just insane. You know, this is a population that is giant. It’s, like, one in two people are women. At some point, we pretty much all go through menopause, some people earlier, some later. 

And for a lot of us, it’s a really painful, disruptive thing to experience. And we tend to experience it at a moment when we actually have spending money. So it just ticks all the boxes. And yet I think because of the bias that we see, you know, in the venture land and in the startup world, we just couldn’t get on this opportunity for a really long time. So I’ve been very excited to see companies like that really have breakout success. 

LEE: First off, you know, I think in terms of hits and misses from our book. One hit is we did think a lot about the idea that patients directly would be empowered by AI. And, you know, we had a whole chapter on this, and it was something that I think has really turned out to be true, and I think it will become more true. But one big miss is we actually didn’t think about what we were just talking about, about like who and when would this happen? And the specific focus on women, women’s health, I think is something that we missed.  

And I think one of the reasons I sought you out for this conversation is if I remember your own personal history, you essentially transitioned from journalism to venture investing at about the same time that you yourself were having a very intense period of engagement with health because of your own pregnancy. And so if you don’t mind, I’d like to get into your own experience with healthcare through pregnancy, your own experiences raising children, and how that has informed your relationship with digital health and the investing and advising that you do today. 

FARR: Yeah, it’s great question. And I actually was somebody who, you know, wrote a lot while I was kind of on maternity leave about this experience because it was such a profound one. You know, I think the reason that pregnancy is so interesting to healthcare companies and systems is because really for a lot of women, it’s their first experience with the hospital.  

Most of us have never stayed in the hospital for any period of time until that moment. Both times I had C-sections, so I was there for a good three or four days. And, you know, I think it’s a really big opportunity for these systems, even if they lose money, many of them lose money on pregnancy, which is a whole different topic, but there is an opportunity to get a whole family on board and keep them kind of loyal. And a lot of that can come through, you know, just delivering an incredible service.  

Unfortunately, I don’t think that we are delivering incredible services today to women in this country. I see so much room for improvement. You know, you see, just look at the data. You see women, you know, still dying in childbirth in this country where in many other developed nations, that’s just no longer the case.  

LEE: Yeah. And what are, in your view, the prime opportunities or needs? What do we need to do if we have a focus on technology to improve that situation?  

FARR: Yeah, I mean, I think there’s definitely an opportunity for, you know, just digital technologies and for remote patient monitoring and just other forms of monitoring. I do think we should look at what other countries have done and really consider things like, you know, three days post-discharge, somebody comes to your home, you know, whether it’s to check on you from a healthcare perspective, both, you know, physical and mental health, but then also make sure that the environment is safe for both the mother and the baby. Simple things like that, that don’t even really require any technology.  

And then there’s certainly opportunities for new forms of, you know, diagnostic tests for things like preeclampsia, postpartum preeclampsia. We could definitely use some new therapeutics in this area. Then, you know, would love to kind of also touch on the opportunity in pediatrics because there I think is an ideal use case for AI. And that’s definitely my reality now. 

LEE: Well, fact, yeah, in fact, I hope I’m not delving into too many personal issues here. But I do remember, I think with your first child, which you had during the height of the COVID pandemic, that your child actually had COVID and actually even lost sense of taste and smell for a period. And, in our book, we had sort of theorized that people would turn possibly to AI for advice to understand what was going on.  

When you look broadly at the kinds of queries that come into a search engine or into something like ChatGPT or Copilot, you do see things along those lines. But at the same time, I had always thought people wouldn’t just use a raw chat bot for these things. People would want an app, perhaps powered by AI, that would be really designed for this. And yet somehow that seems not to be as widespread.  

FARR: Yeah. And I think the word app is a great one that I’d love to, you know, maybe interrogate a little bit because I think that we have been overly reliant on apps. I’ll give you an example. So in a pediatric space, I am a user of an app called Summer Health (opens in new tab) or it’s not an app. Sorry. It’s a text messaging service. [LAUGHTER] And this is the genius. So I just pick up my phone, and I text “Summer” and a pediatrician responds within a matter of minutes. And sometimes it’s a pediatric nurse, but it’s somebody who responds to me. And they say, oh, what’s going on? And I might say, OK, well, this week we had the norovirus. So these are the symptoms. And they might say, you know, I’d love to see an image or a video. And I can text that to them.  

And if a prescription is required, then that goes to a pharmacy near me through another digital application that’s really cool called Photon Health (opens in new tab), where my script is portable, so I can move it around based on what’s open.  

So, through this, I’m getting an incredible experience that’s the most convenient … 

LEE: Wow. 

FARR: I could ever ask for, and there is no app. [LAUGHS] And you could imagine the potential for AI. You know, a company like this is probably getting so many questions about a norovirus or COVID or RSV [Respiratory Syncytial Virus], and is, I’m sure, starting to think about kind of ways in which AI could be very useful in this regard. And you don’t need a pediatrician or pediatric nurse answering every question. Perhaps there’s like sophisticated triaging to determine which questions should go to the human expert.  

But, you know, again, back to this app question, like, I think we have too many. Like, it’s just … like from a user experience perspective, just having to find the app, log into the app. Sometimes there’s just layers of authentication. Then you have to remember your password. [LAUGHTER] And it’s just, you know, it’s just too many steps. And then there’s like 50 of them for all kinds of different things. 

LEE: Yes. Well, and you have to also go to an app store, download the thing.  

FARR: Go to the app store down. It’s just too many steps.  

LEE: Yes. 

FARR: So, like, I, you know, I recognize that HIPAA exists. If there is any kind of claim involved, then, you know, you need an app because you got privacy to think about and compliance, but like, in this wave of consumerization of healthcare, there’s a lot more that’s possible. And so I’d love to see people experimenting a bit more with the form factor. And I think once we do that, we could open up a lot more interesting applications with AI, because you’ll see so much more usage day to day than you will if you require any of this kind of gatekeeping with an app. 

LEE: It’s so interesting to hear you say this because one thing that I’ve thought—and I’ve actually even expressed publicly in some venues—is one logical endpoint for AI as we understand it today is that apps become unnecessary. We might still have machines that, you know, you hold in the palm of your hand, but it’s just a machine that does what you want it to do.  

Of course, the business model implications are pretty profound. So for that particular text messaging service, do you understand what their business model is? You know, how are they sustaining themselves? 

FARR: Consumer, it’s all cash pay. It’s cash pay. You just pay a subscription. And, you know, there are certainly kind of privacy requirements, you know, related to kind of federal and state, but you could consent to be able to do something like this. And, you know, companies like this have teams of lawyers that kind of think through how do you make something like this happen. But it’s possible because of this cash pay element that really underlies that. And I think that is a growing trend.  

You know, I was literally sitting with a benefits consultant a few weeks ago, and he was saying to me, like, “I tell all my friends and family, just don’t use your insurance at all, unless it’s for like a very high price thing, like a medical procedure that’s expensive or a surgery.” He said, for everything else, I just pay cash. I pay cash for all my primary care. I pay cash for, you know, basic generic, you know, prescription medications that, you know, it’s like a few cents to manufacture.  

And I’m sort of getting there, too, where I just kind of increasingly am relying on cash pay. And I think that sort of opens up a world of opportunity for just innovation related to user experience that could really bring us to this place that you mentioned where there is no app. You literally just text or, you know, you use your voice, and you say, “I need a restaurant reservation,” and it’s done.  

LEE: Mm-hmm. Yeah. 

FARR: And it’s that simple, right? And the sort of appification of everything, you know, was a important kind of evolution or moment in technology that is undeniable. But I totally agree with you that I think we might be moving past that. 

LEE: On this idea of cash, there is a little bit of a fatigue, on the other hand, with—for consumers; let me just speak as a consumer—I can’t keep track anymore of all the subscriptions I have. And so are we just trading one form of, you know, friction for another? 

FARR: Yeah, that’s a great point. But there are things that, you know, I think there are those moments where you continue to pay a subscription because it’s just something that’s chronic. You know, it’s just relevant to you. You know, pediatrics is a great example. At some point, like I won’t need a pediatrician on demand, which is what I have now, maybe when my kids are a little older, and we’re not just a cesspool of various kind of viruses at home. [LAUGHTER] But again, back to your point about, you know, the sort of moments of just, like, natural engagement, I think there’s also a moment there … there are areas or parts of our lives where, like primary care, where it’s just more longitudinal.  

And it makes sense to pay on a kind of subscription basis. Like our system is messed up because there’s just messed up incentives, right. And a subscription to me is very pure. [LAUGHTER] Like it’s you’re just saying, “I’m paying for a service that I want and need.” And then the company is saying, “OK, let me make this service as efficient and great and affordable for you as I possibly can.” And to me, that’s like a very, like refreshing trade. And I feel the same way, by the way, in my media business, which, you know, definitely has a subscription element. And it just means a lot when someone’s willing to say like this content’s worth paying for.  

LEE: Yes. 

FARR: It doesn’t work for everything, but I think it works for things that, you know, have that long-term payoff. 

LEE: Yeah, I really love that. And if I have one regret about the chapter on kind of the consumer experience from our book—I think all of this seems obvious in retrospect—you know, I wish we had tried to understand, you know, this aspect of the consumer experience, that people might actually have just online experiences that they would pay a monthly fee or an annual fee for. Because it also hits on another aspect of consumer, which is this broad—it’s actually now a national issue in healthcare—about price transparency.  

And this is another thing that I think you’ve thought about and written about, both the positives and negatives of this. I remember one blog post you made that talked about the issue of churn in digital health. And if I remember correctly, you weren’t completely certain that this was a good thing for the emerging digital health ecosystem. Can you say more about this idea of churn? 

FARR: Yeah, I mean, you know, I’ve been writing for a long time and thinking for a long time about the buyers of a lot of these kind of digital health companies, like who are the customers? And there was a long period where it was, it was really the self-insured employer, like Microsoft, being a sort of customer of these solutions because they wanted to provide a great array of health benefits for their own employees.  

And that was, you know, for a long time, like 10 or 15 years, you know, big companies that have now gone public, and it seemed like a faster timeline to be able to sell relative to health systems and, you know, health plans and other groups. And I’ve now kind of been on the forefront of saying that this channel is kind of dead. And one of the big reasons is just, you know, there’s no difference, I would say to what you see kind of in the payer lane, which is that churn is a big problem. People used to stay at jobs for 20, 30, 40 years, … 

LEE: Right. 

FARR: … and then you’d retire and have great benefits. And so it kind of made sense that your company was responsible for the healthcare that you received. And now I think the last time I looked at the Bureau of Labor Statistics, it’s around four years, a little bit less than four years. So what can you do in four years? [LAUGHS] 

I just read an interesting analysis on GLP-1s, these medications now that obviously are everywhere in tackling type two diabetes, and obesity is kind of the main, seems to be the hot use case. But, you know, I’m reading analysis around ROI that it’s 15, over 15 years, to see an ROI if you are, you know, a system or a plan or employer that chooses to pay for this. So how does that equate when you don’t keep an employee around for more than four?  

LEE: Yep. 

FARR: So I think it’s just left employers in a really bad place of having to make a bunch of tradeoffs and, you know, employees are demanding, we want access to these things. And they’re saying, well, our healthcare costs just keep going up and up and up. You know, we have inflation to contend with and we’re not seeing, you know, the analysis that it necessarily makes sense for us to do so. So that’s what I have, you know, been sort of harping on about with this churn issue that I’m seeing. 

LEE: Well, I have to tell you, it really, when I first started reading about this from you, it really had a profound impact on my thinking, my thought process. Because one of the things that we dream about is this idea that’s been present actually for decades in the healthcare world of this concept of real-world evidence, RWE. And that is this dream that now that we’ve digitized so much health experience, we should be able to turn all that digital data from people’s health experiences into new medical knowledge.  

But the issue of churn that I think that I would credit you introducing me to calls that into question because you’re right. Over a four-year period, you don’t get the longitudinal view of a person’s health that gives you the ability to get those medical insights. And so something needs to change there. But it’s very much tied to what consumers want to do. Consumers move around; they change jobs.  

FARR: Yes.  

LEE: If it’s cash-based, they’ll be shopping based on all sorts of things. And so it … 

FARR: And so the natural end of all this, it’s two words: single payer. [LAUGHS] But we don’t want to go there as a country. So, you know, it sort of left us in this kind of murky middle. And I think a lot about, kind of, what kind of system we’ll end up having. What I don’t think is possible is that this current one is sustainable.  

LEE: You know, I do think in terms of the payer of CMS [Centers for Medicare and Medicaid Services], Medicare and Medicaid services, the amount of influence that they exert on health spending in the US has been increasing steadily year by year. And in a sense, you could sort of squint and view that as a slow drift towards some element of single payer. But it’s definitely not so intentional or organized right now.  

While we’re talking about these sorts of trends, of course, another big trend is the graying of America. And we’re far from alone, China, and much of the Orient, Europe, UK, people are getting older. And from the consumer-patient perspective, this brings up the challenge, I think, that many people have in caring for elderly loved ones.  

And this seems to me, like women’s health, to be another area where if I were starting a new digital health company, I would think very seriously about that space because that’s another space where there can be extreme intensity of engagement with the healthcare system. Do you as both a human being and consumer but also as an investor, do you think about that space at all? 

FARR: Oh, yes, all the time. And I do think there’s incredible opportunity here.  

And it’s probably because of the same kind of biases that exist that, you know, didn’t allow us to see the menopause opportunity, I think we’re just not seeing this as being as big as it is. And like you said, it’s not just an American problem. It’s being felt across the world.  

And I do think that there are some, you know, I’ve seen some really interesting stuff lately. Was recently spending some time with a company called Cherish Health (opens in new tab) out of Boston, and they’re using AI and radar-based sensing technologies to just be able to stick a device and like really anywhere in the person’s home. And it just like passively is able to detect falls and also kind of monitor kind of basic health metrics. And because it’s radar, it can operate through walls. So even if you’re in the bathroom, it still works, which has been a big problem with a lot of these devices in the past.  

And then, you have to have really advanced kind of AI and, you know, this sort of technology to be able to glean whether it’s a true fall or, you know, that’s really, you need help or it’s, you know, just the person sitting down on the floor to play with their grandchild. So things like this are, they’re still early, but I think really exciting. And we’re going to see a lot more of that in addition to, you know, some really interesting companies that are trying to think more about sort of social needs that are not healthcare needs, but you know, this, this population needs care, like outside of just, you know, medical treatment. They oftentimes may be experiencing homelessness, they might experience food insecurity, there might be a lack of just caregivers in their life. And so, you know, there are definitely some really interesting businesses there, as well.  

And then kind of a, you know, another trend that I think we’ll see a lot more is that, you know, countries are freaking out about the lack of babies being born, which you need to be able to … you know, I recognize climate change is a huge issue, but you also need babies to be born to support this aging population. So I think we’re going to see, you know, a lot more interest from these administrations around, you know, both like child tax credits and various policies to support parents but then also IVF [in vitro fertilization] and innovation around technology in the fertility space.  

LEE: All right. So we’re starting to run towards the end of our time together. So I’d like to get into maybe a couple more provocative or, you know, kinds of questions. So first, and there’s one that’s a little bit dark and another that’s much lighter. So let me start with the darker one so we can have a chance to end on a lighter note. I think one of the most moving pieces I’ve read from you recently was the open letter to your kids about the assassination of Brian Thompson (opens in new tab), who’s a senior executive of UnitedHealth Group. And so I wonder if you’re willing to share, first off, what you wrote there and then why you felt it was important to do that. 

FARR: Yeah. So, you know, I thought about just not saying anything. That was my original intention because it was just, you know, that moment that it happened, it was just so hot button. And a lot of people have opinions, and Twitter was honestly a scary place, just with the things that people were saying about this individual, who, you know, I think just like had a family and friends and a lot of my network knew him and felt really personally impacted by this. And I, you know, it was just a really sad moment, I think, for a lot of reasons.  

And then I just kind of sat down one evening and I wrote this letter to my kids that basically tried to put a lot of this in context. Like what … why are people feeling this way about our healthcare system? You know, why was all this sort of vitriol being really focused on this one individual? And then, you know, I think one of the things I sort of argued in this letter was that there’s lots of ways to approach innovation in the space. You can do it from the outside in, or you can do it from the inside out.  

And I’ll tell you that a lot of like, I got a lot of emails that week from people who were working at health plans, like UnitedHealth employees, some of them in their 20s, you know, they were recent kind of grads who’d gone to work at this company. And they said, you know, I felt like I couldn’t tell my friends, kind of, where I worked that week. And I emailed back and said, “Look, you’re learning healthcare. You are in an incredible position right now. Like whether you choose to stay your current company or you choose to leave, like you, you understand like the guts and the bowels of healthcare because you’re working at the largest healthcare company in the world. So you’re in an enviable position. And I think you are going to be able to effect change, like, more so than anyone else.” And that was part of what I wrote in this letter, that, you know, we should all agree that the system is broken, and we could do better. Nothing about what happened was OK. And also, like, let’s admire our peers and colleagues that are going into the trenches to learn because I genuinely believe those are the people that, you know, have the knowledge and the contacts and the network to be able to really kind of get change moving along, such desperately needed change. 

LEE: All right. So now one thing I’ve been asking every guest is about the origin story with respect to your first encounter with generative AI. How did that happen, and what were your first sort of experiences like? You know, what emotionally, intellectually, what went through your mind? 

FARR: So probably my first experience was I was really struggling with the title for my book. And I told ChatGPT what my book was about and what I wanted the title to evoke and asked it for recommendations. And then, I thought the first, like, 20 were actually pretty good. And I was able to say, can you make it a bit more witty? Can you make it more funny? And it spat back out some quite decent titles. And then what was interesting is that it just got worse and worse, like, over time and just ended up, like, deeply cheesy. [LAUGHTER] 

And so it sort of both like made me think that this could be a really useful prompt for just brainstorming. But then either it does seem to be some weird thing with AI where, like the more you push it on the same question, it just, like, it doesn’t … it seems to have sparked the most creativity in the first few tries, and then it just gets worse. And maybe you know more about this than I would. You certainly know more about this than I do. But that’s been my kind of general experience of it thus far. 

LEE: Mm-hmm. But would you say you were more skeptical or awe-inspired? What were the emotions at that moment? 

FARR: Um, you know, it was better than, like, a lot of my ideas. [LAUGHTER] So I definitely felt like it was from that perspective very impressive. But then, you know, it seemed to have the same human, like I said, we all kind of run out of ideas at some point and, you know, it turns out, so do the machines.  

So that was interesting in and of itself. And I ended up picking, I think a title that was like sort of, you know, inspired by the AI suggestions, but was definitely had its own twist that was my own. 

LEE: Well, Chrissy, I’ve never known you as someone who runs out of ideas, but this has been just great. As always, I always learn a lot when I have a chance to interact with you or read your writings. And so, thank you again for joining. Just really, really appreciate it. 

FARR: Of course, and next time I want to have you on my podcast because I have a million questions for you, too.   

LEE: Sure, anytime. 

FARR: Amazing. OK, I’ll hold you to that. Thanks so much for having me on. 

LEE: I’ve always been impressed not only with Chrissy’s breadth and depth of experience with the emerging tech trends that affect the health industry, but she’s also a connector to key decision-makers in nearly every sector of healthcare. This experience, plus her communication abilities, make it no surprise that she’s sought out for help in a range of go-to-market, investor relations, social media, content development, and communications issues. 

Maybe it shouldn’t be a surprise, but one thing I learned from our conversation is that the business of direct-to-consumer health is still emerging. It’s far from mature. And you can see that Chrissy and her venture-investing colleagues are still trying to figure out what works. Her discussion, for example, on cash-only health delivery and the idea that consumers might not want another app on their phones were indicative of that.  

Another takeaway is that some areas, such as pre- and postnatal care, menopause, elder care, and other types of what the health industry might call subacute care are potentially areas where not only AI might find the most impact but also where there’s sufficient engagement by consumers to make it possible to sustain the business. 

When Carey, Zak, and I started writing our book, one of the things that we started off with was based on a story that Zak had written concerning his 90-year-old mother. And of course, as I had said in an earlier episode of this podcast, that was something that really touched me because I was having a similar struggle with my father, who at the time was 89 years old. 

One of the things that was so difficult about caring for my father is that he was living in Los Angeles, and I was living up in the Pacific Northwest. And my two sisters also lived far away from Los Angeles, being in Pittsburgh and in Phoenix.  

And so as the three of us, my two sisters and I, tried to navigate a fairly complex healthcare system involving a primary care physician for my father plus two specialists, I have to say over a long period of illness, a lot of things happen, including the fraying of relationships between three siblings. What was so powerful for us, and this is where this idea of patient empowerment comes in, is when we could give all of the data, all of the reports from the specialist, from the primary care physician, other information, give it to GPT-4 and then just ask the question, “We’re about to have a 15-minute phone call with one of the specialists. What are the most important two or three things we should ask about?” Doing that just brings down the temperature, eliminates a potential source of conflict between siblings who are all just wanting to take care of their father. 

And so as we think about the potential of AI in medicine, this concept of patient empowerment, while we’ve learned in this episode, is still emerging, I think in the long run could be the most important long-term impact of this new age of AI. 

I’d like to say thank you again to Dave and Chrissy for sharing their stories and insights. And to our listeners, thank you for joining us. We have some really great conversations planned for the coming episodes, including a discussion on regulations, norms, and ethics developing around AI and health. We hope you’ll continue to tune in.  

Until next time. 

Transcript

PETER LEE: “It’s hard to convey the huge complexity of today’s healthcare system. Processes and procedures, rules and regulations, and financial benefits and risks all interact, evolve, and grow into a giant edifice of paperwork that is well beyond the capability of any one human being to master. This is where the assistance of an AI like GPT-4 can be not only useful—but crucial.”   

This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.  

Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?   

In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.

The passage I read at the top there is from Chapter 7 of the book, “The Ultimate Paperwork Shredder.”  

Paperwork plays a particularly important role in healthcare. It helps convey treatment information that supports patient care, and it’s also used to help demonstrate that providers are meeting regulatory responsibilities, among other things. But if we’re being honest, it’s taxing—for everyone—and it’s a big contributor to the burnout our clinicians are experiencing today. Carey, Zak, and I identified this specific pain point as one of the best early avenues to pursue as far as putting generative AI to good work in the healthcare space.  

In this episode, I’m excited to welcome Dr. Matt Lungren and Seth Hain to talk about matching technological advancements in AI to clinical challenges, such as the paperwork crisis, to deliver solutions in the clinic and in the health system back office.  

Matt is the chief scientific officer for Microsoft Health and Life Sciences, where he focuses on translating cutting-edge technology, including generative AI and cloud services, into innovative healthcare applications. He’s a clinical interventional radiologist and a clinical machine learning researcher doing collaborative research and teaching as an adjunct professor at Stanford University. His scientific work has led to more than 200 publications, including work on new computer vision and natural language processing approaches for healthcare.  

Seth is senior vice president of research and development at Epic, a leading healthcare software company specializing in electronic health record systems, also known as EHR, as well as other solutions for connecting clinicians and patients. During his 19 years at Epic, Seth has worked on enhancing the core analytics and other technologies in Epic’s platforms as well as their applications across medicine, bringing together his graduate training in mathematics and his dedication to better health.  

I’ve had the pleasure of working closely with both Matt and Seth. Matt, as a colleague here at Microsoft, really focused on our health and life sciences business. And Seth, as a collaborator at Epic, as we embark on the questions of how to integrate and deploy generative AI into clinical applications at scale.   

Here’s my conversation with Dr. Matt Lungren:  

LEE: Matt, welcome. It’s just great to have you here. 

MATTHEW LUNGREN: Thanks so much, Peter. Appreciate being here. 

LEE: So, I’d like to just start just talking about you. You know, I had mentioned your role as the chief scientific officer for Microsoft Health and Life Sciences. Of course, that’s just a title. So, what the heck is that? What is your job exactly? And, you know, what does a typical day at work look like for you? 

LUNGREN: So, really what you could boil my work down to is essentially cross collaboration, right. We have a very large company, lots of innovation happening all over the place, lots of partners that we work with and then obviously this sort of healthcare mission.

And so, what innovations, what kind of advancements are happening that can actually solve clinical problems, right, and sort of kind of direct that. And we can go into some examples, you know, later. But then the other direction, too, is important, right. So, identifying problems that may benefit from a technologic application or solution and kind of translating that over into the, you know, pockets of innovation saying, “Hey, if you kind of tweaked it this way, this is something that would really help, you know, the clinical world.”  

And so, it’s really a bidirectional role. So, my day to day is … every day is a little different, to be honest with you. Some days it’s very much in the science and learning about new techniques. On the other side, though, it can be very much in the clinic, right. So, what are the pain points that we’re seeing? Where are the gaps in the solutions that we’ve already rolled out? And, you know, again, what can we do to make healthcare better broadly? 

LEE: So, you know, I think of you as a technologist, and, Matt, you and I actually are colleagues working together here at Microsoft. But you also do spend time in the clinic still, as well, is that right? 

LUNGREN: You know, initially it was kind of a … very much a non-negotiable for me … in sort of taking an industry role. I think like a lot of, you know, physicians, you know, we’re torn with the idea of like, hey, I spent 20 years training. I love what I do, you know, with a lot of caveats there in terms of some of the administrative burden and some of the hassle sometimes. But for the most part, I love what I do, and there’s no greater feeling than using something that you trained years to do and actually see the impact on a human life. It’s unbelievable, right.  

So, I think part of me was just, like, I didn’t want to let that part of my identity go. And frankly, as I often say, to this day, I walk by a fax machine in our office today, like in 2025.  

So just to be extra clear, it really grounds me in, like, yes, I love the possibilities. I love thinking about what we can do. But also, I have a very stark understanding of the reality on the ground, both in terms of the technology but also the burnout, right. The challenges that we’re facing in taking care of patients has gotten, you know, much, much more difficult in the last few years, and, you know, I like to think it keeps my perspective, yeah. 

LEE: You know, I think some listeners to this podcast might be surprised that we have doctors on staff in technical roles at Microsoft. How do you explain that to people? 

LUNGREN: [LAUGHS] Yeah, no, yeah, it is interesting. I would say that, you know, from, you know, the legacy Nuance [1] world, it wasn’t so far-fetched that you have physicians that were power users and eventually sort of, you know, became, “Hey, listen, I think this is a strategic direction; you should take it” or whatever. And certainly maybe in the last, I want to say, five years or so, I’ve seen more and more physicians who have, you know, taken the time, sometimes on their own, to learn some of the AI capabilities, learn some of the principles and concepts; and frankly, some are, you know, even coding solutions and leading companies.

So, I do think that that has shifted a bit in terms of like, “Hey, doctor, this is your lane, and over here, you know, here’s a technical person.” And I think that’s fused quite a bit more.  

But yeah, it is an unusual thing, I think, in sort of how we’ve constructed what at least my group does. But again, I can’t see any other way around some of the challenges.  

I think, you know, an anecdote I’d like to tell you, when I was running the AIMI [Artificial Intelligence in Medicine and Imaging] Center, you know, we were bringing the medical school together with the computer science department, right, at Stanford. And I remember one day a student, you know, very smart, came into my office, you know, a clinical day or something, and he’s like, is there just, like, a book or something where I can just learn medicine? Because, like, I feel like there’s a lot of, like, translation you have to do for me.  

It really raised an important insight, which is that you can learn the, you know, medicine, so to speak. You know, go to med school; you know, take the test and all that. But it really … you don’t really understand the practice of medicine until you are doing that.  

And in fact, I even push it a step further to say after training those first two or three years of … you are the responsible person; you can turn around, and there’s no one there. Like, you are making a decision. Getting used to that and then having a healthy respect for that actually I think provides the most educational value of anything in healthcare.  

LEE: You know, I think what you’re saying is so important because as I reflect on my own journey. Of course, I’m a computer scientist. I don’t have medical training, although at this point, I feel confident that I could pass a Step 1 medical exam.  

LUNGREN: I have no doubt. [LAUGHS] 

LEE: But I think that the tech industry, because of people like you, have progressed tremendously in having a more sophisticated and nuanced understanding of what actually goes on in clinic and also what goes on in the boardrooms of healthcare delivery organizations. And of course, at the end of the day, I think that’s really been your role.  

So roughly speaking, your job as an executive at a big tech company has been to understand what the technology platforms need to be, particularly with respect to machine learning, AI, and cloud computing, to best support healthcare. And so maybe let’s start pre-GPT-4, pre-ChatGPT, and tell us a little bit, you know, about maybe some of your proudest moments in getting advanced technologies like AI into the clinic. 

LUNGREN: You know, when I first started, so remember, like you go all the way back to about 2013, right, my first faculty job, and, you know, we’re building a clinical program and I, you know, I had a lot of interest in public health and building large datasets for pop [population] health, etc. But I was doing a lot of that, you know, sort of labeling to get those insights manually, right. So, like, I was the person that you’d probably look at now and say, “What are you doing?” Right?  

So … but I had a complete random encounter with Andrew Ng, who I didn’t know at the time, at Stanford. And I, you know, went to one of the seminars that he was holding at the Gates building, and, you know, they were talking about their performance on ImageNet. You know, cat and dog and, you know, tree, bush, whatever. And I remember sitting in kind of the back, and I think I maybe had my scrubs on at the time and just kind of like, what? Like, why … like, this … we could use this in healthcare, you know. [LAUGHS]  

But for me, it was a big moment. And I was like, this is huge, right. And as you remember, the deep learning really kind of started to show its stuff with, you know, Fei-Fei Li’s ImageNet stuff.

So anyway, we started the collaboration that actually became a NIDUS. And one of the first things we worked on, we just said, “Listen, one of the most common medical imaging examinations in the world is the chest x-ray.” Right? Two, three billion are done every year in the world, and so is that not a great place to start?

And of course, we had a very democratizing kind of mission. As you know, Andrew has done a lot of work in that space, and I had similar ambitions. And so, we really started to focus on bringing the, you know, the sort of the clinical and the CS together and see what could be done.  

So, we did CheXNet. And this is, remember this is around the time when, like, Geoffrey Hinton was saying things like we should stop training radiologists, and all this stuff was going on. [LAUGHTER] So there’s a lot of hype, and this is the narrow AI days just to remind the audience.  

LEE: How did you feel about that since you are a radiologist? 

LUNGREN: Well, it was so funny. So, Andrew is obviously very prolific on social media, and I was, who am I, right? So, I remember he tagged me. Well, first he said, “Matt, you need to get a Twitter account.” And I said OK. And he tagged me on the very first post of our, what we call, CheXNet that was kind of like the “Hello, World!” for this work.  

And I remember it was a clinical day. I had set my phone, as you do, outside the OR. I go in. Do my procedure. You know, hour or so, come back, my phone’s dead. I’m like, oh, that’s weird. Like I had a decent charge. So, you know, I plug it in. I turn it on. I had like hundreds of thousands of notifications because Andrew had tweeted out to his millions or whatever about CheXNet.  

And so, then of course, as you point out, I go to RSNA that year, which is our large radiology conference, and that Geoffrey Hinton quote had come out. And everyone’s looking at me like, “What are you doing, Matt?” You know, like, are you coming after our specialty? I’m like, “No, no,” that’s, [LAUGHS] you know, it’s a way to interpret it, but you have to take a much longer horizon view, right.  

LEE: Well, you know, we’re going to, just as an enticement for listeners to this podcast to listen to the very end, I’m going to pin you down toward the end on your assessment of whether Geoffrey Hinton will eventually be proven right or not. [LAUGHTER] But let’s take our time to get there.  

Now let’s go ahead and enter the generative AI era. When we were first exposed to what we now know of as GPT-4—this was before it was disclosed to the world—a small number of people at Microsoft and Microsoft Research were given access in order to do some technical assessment.  

And, Matt, you and I were involved very early on in trying to assess what might this technology mean for medicine. Tell us, you know, what was the first encounter with this new technology like for you?  

LUNGREN: It was the weirdest thing, Peter. Like … I joined that summer, so the summer before, you know, the actual GPT came out. I had literally no idea what I was getting into.  

So, I started asking it questions, you know, kind of general stuff, right. Just, you know, I was like, oh, all right, it’s pretty good. And so, then I would sort of go a little deeper. And eventually I got to the point where I’m asking questions that, you know, maybe there’s three papers on it in my community, and remember I’m a sub-sub specialist, right, pediatric interventional radiology. And the things that we do in vascular malformations and, you know, rare cancers are really, really strange and not very commonly known.  

And I kind of walked away from that—first I said, can I have this thing, right? [LAUGHS]  

But then I, you know, I don’t want to sound dramatic, but I didn’t sleep that well, if I’m being honest, for the first few nights. Partially because I couldn’t tell anybody, except for the few that I knew were involved, and partially because I just couldn’t wrap my head around how we went from what I was doing in LSTMs [long short-term memory networks], right, which was state of the artish at the time for NLP [natural language processing].  

And all of a sudden, I have this thing that is broadly, you know, domain experts, you know, representations of knowledge that there’s no way you could think of it would be in distribution for a normal approach to this.  

And so, I really struggled with it, honestly. Interpersonally, like, I would be like, uh, well, let’s not work on that. They’re like, why not? You were just excited about it last week. I’m like, I don’t know. I think that we could think of another approach later. [LAUGHS]  

And so yeah, when we were finally able to really look at some of the capabilities and really think clearly, it was really clear that we had a massive opportunity on our hands to impact healthcare in a way that was never possible before. 

LEE: Yeah, and at that time you were still a part of Nuance. Nuance, I think, was in the process of being acquired by Microsoft. Is that right?  

LUNGREN: That’s right.  

LEE: And so, of course, this was also a technology that would have profound and very direct implications for Nuance. How did you think about that? 

LUNGREN: Nuance, for those in the audience who don’t know, for 25 years was, sort of, the medical speech-to-text thing that all, you know, physicians used. But really the brass ring had always been … and I want to say going back to like 2013, 2014, Nuance had tried to figure out, OK, we see this pain point. Doctors are typing on their computers while they’re trying to talk to their patients, right.  

We should be able to figure out a way to get that ambient conversation turned into text that then, you know, accelerates the doctor … takes all the important information. That’s a really hard problem, right. You’re having a conversation with a patient about their knee pain, but you’re also talking about, you know, their cousin’s wedding and their next vacation and their dog is sick or whatever and all that gets recorded, right.  

And so, then you have to have the intelligence/context to be able to tease out what’s important for a note. And then it has to be at the performance level that a physician who, again, 20 years of training and education plus a huge, huge amount of, you know, need to get through his cases efficiently, that’s a really difficult problem.  

And so, for a long time, there was a human-in-the-loop aspect to doing this because you needed a human to say, “This transcript’s great, but here’s actually what needs to go on the note.” And that can’t scale, as you know.  

When the GPT-4, you know, model kind of, you know, showed what it was capable of, I think it was an immediate light bulb because there was no … you can ask any physician in your life, anyone in the audience, you know, what are your … what is the biggest pain point when you go to see your doctor? Like, “Oh, they don’t talk to me. They don’t look me in the eye. They’re rushing around trying to finish a note.”  

If we could get that off their plate, that’s a huge unlock, Peter. And I think that, again, as you know, it’s now led to so much more. But that was kind of the initial, I think, reaction. 

LEE: And so, maybe that gets us into our next set of questions, our next topic, which is about the book and all the predictions we made in the book. Because Carey, Zak, and I—actually we did make a prediction that this technology would have a huge impact on this problem of clinical note-taking.  

And so, you’re just right in the middle of that. You’re directly hands-on creating, I think, what is probably the most popular early product for doing exactly that. So, were we right? Were we wrong? What else do we need to understand about this? 

LUNGREN: No, you were right on. I think in the book, I think you called it like a paper shredder or something. I think you used a term like that. That’s exactly where the activity is right now and the opportunity.  

I’ve even taken that so far as to say that when folks are asking about what the technology is capable of doing, we say, well, listen, it’s going to save time before it saves lives. It’ll do both. But right now, it’s about saving time.  

It’s about peeling back the layers of the onion that if you, you know, put me in where I started medicine in 2003, and then fast-forward and showed me a day in the life of 2025, I would be shocked at what I was doing that wasn’t related to patient care, right. So, all of those layers that have been stacked up over the years, we can start finding ways to peel that back. And I think that’s exactly what we’re seeing.

And to your point, I think you mentioned this, too, which is, well, sure, we can do this transcript, and we can turn a note, but then we can do other things, right. We can summarize that in the patient’s language or education level of choice. We can pend orders. We can eventually get to a place of decision support. So, “Hey, did you think about this diagnosis, doctor?” Like those kinds of things.  

And all those things, I think you highlighted beautifully, and again, it sounds like with, you know, a lot of, right, just kind of guesswork and prediction, but those things are actually happening every single day right now.  

LEE: Well, so now, you know, in this episode, we’re really trying to understand, you know, where the technology industry is in delivering these kinds of things. And so from your perspective, you know, in the business that you’re helping to run here at Microsoft, you know, what are the things that are actually shipping as product versus things that clinicians are doing, let’s say, off label, just by using, say, ChatGPT on their personal mobile devices, and then what things aren’t happening? 

LUNGREN: Yeah. I’ll start with the shipping part because I think you, again, you know my background, right. Academic clinician, did a lot of research, hadn’t had a ton of product experience.  

In other words, like, you know, again, I’m happy to show you what benchmarks we beat or a new technique or, you know, get a grant to do all this, or even frankly, you know, talk about startups. But to actually have an audience that is accustomed to a certain level of performance for the solutions that they use, to be able to deliver something new at that same level of expectation, wow, that’s a big deal.  

And again, this is part of the learning by, you know, kind of being around this environment that we have, which is we have this, you know, incredibly focused, very experienced clinical product team, right.

And then I think on the other side, to your point about the general-purpose aspect of this, it’s no secret now, right, that, you know, this is a useful technology in a lot of different medical applications. And let’s just say that there’s a lot of knowledge that can be used, particularly by the physician community. And I think the most recent survey I saw was from the British Medical Journal, which said, hey, you know, which doctors are using … are you willing to tell us, you know, what you’re doing? And it turns out that folks are, what, 30% or so said that they were using it regularly in clinic [2]. And again, this is the general, this is the API or whatever off the shelf.

And then frankly, when they ask what they’re using it for, tends to be things like, “Hey, differential, like, help me fill in my differential or suggest … ” and to me, I think what that created, at least—and you’re starting to see this trend really accelerate in the US especially—is, well, listen, we can’t have everybody pulling out their laptops and potentially exposing, you know, patient information by accident or something to a public API.  

We have to figure this out, and so brilliantly, I think NYU [New York University] was one of the first. Now I think there’s 30 plus institutions that said, listen, “OK, we know this is useful to the entire community in the healthcare space.” Right? We know the administrators and nurses and everybody thinks this is great.  

We can’t allow this sort of to be a very loosey-goosey approach to this, right, given this sort of environment. So, what we’ll do is we’ll set up a HIPAA-compliant instance to allow anyone in the community—you know, in the health system—to use the models, and then whatever, the newest model comes, it gets hosted, as well.  

And what’s cool about that—and that’s happened now a lot of places—is that at the high level … first of all, people get to use it and experiment and learn. But at the high level, they’re actually seeing what are the common use cases. Because you could ask 15 people and you might get super long lists, and it may not help you decide what to operationalize in your health system.  

LEE: But let me ask you about that. When you observe that, are there times when you think, “Oh, some specific use cases that we’re observing in that sort of organic way need to be taken into specialized applications and made into products?” Or is it best to keep these things sort of, you know, open-chat-interface types of general-purpose platform?  

LUNGREN: Honestly, it’s both, and that’s exactly what we’re seeing. I’m most familiar with Stanford, kind of, the work that Nigam Shah leads on this. But he, he basically, … you know, there’s a really great paper that is coming out in JAMA, but basically saying, “Here’s what our workforce is using it for. Here are the things in the literature that would suggest what would be popular.”  

And some of those line up, like helping with a clinical diagnosis or documentation, but some of them don’t. But for the most part, the stuff that flies to the top, those are opportunities to operationalize and productize, etc. And I think that’s exactly what we’re seeing. 

LEE: So, let’s get into some of the specific predictions. We’ve, I think, beaten note-taking to death here. But there’s other kinds of paperwork, like filling out prior authorization request forms or referral letters, an after-visit note or summary to give instructions to patients, and so on. And these were all things that we were making guesses in our book might be happening. What’s the reality there? 

LUNGREN: I’ve seen every single one of those. In fact, I’ve probably seen a dozen startups too, right, doing exactly those things. And, you know, we touched a little bit on translation into the actual clinic. And that’s actually another thing that I used to kind of underappreciate, which is that, listen, you can have a computer scientist and a physician or nurse or whatever, like, give the domain expertise, and you think you’re ready to build something.  

The health IT [LAUGHS] is another part of that Venn diagram that’s so incredibly critical, and then exactly how are you going to bring that into the system. That’s a whole new ballgame. 

And so I do want to do a callout because the collaboration that we have with Epic is monumental because here, you have the system of record that most physicians, at least in the US, use. And they’re going to use an interface and they’re going to have an understanding of, hey, we know these are pain points, and so I think there’s some really, really cool, you know, new innovations that are coming out of the relationship that we have with Epic. And certainly the audience may be familiar with those, that I think will start to knock off a lot of the things that you predicted in your book relatively soon. 

LEE: I think most of the listeners to this podcast will know what Epic is. But for those that are unfamiliar with the health industry, and especially the technology foundation, Epic is probably the largest provider of electronic health record systems. And, of course, in collaboration with you and your team, they’ve been integrating generative AI quite a bit. Are there specific uses that Epic is making and deploying that get you particularly excited? 

LUNGREN: First of all, the ambient note generation, by the way, is integrated into Epic now. So like, you know, it’s not another screen, another thing for physicians. So that’s a huge, huge unlock in terms of the translation.

But then Epic themselves, so they have, I guess, on the last roadmap that they talked [about], more than 60, but the one that’s kind of been used now is this inbox response. 

So again, maybe someone might not be familiar with, why is it such a big deal? Well, if you’re a physician, you already have, you know, 20 patients to see that day and you got all those notes to do, and then Jevons paradox, right. So if you give me better access to my doctor, well, maybe I won’t make an appointment. I’m just going to send him a note and this is kind of this inbox, right.  

So then at the end of my day, I got to get all my notes done. And then I got to go through all the inbox messages I’ve received from all of my patients and make sure that they’re not like having chest pain and they’re blowing it off or something.  

Now that’s a lot of work and the cold start problem of like, OK, I to respond to them. So Epic has leveraged this system to say, “Let me just draft a note for you,” understanding the context of, you know, what’s going on with the patient, etc. And you can edit that and sign it, right. So you can accelerate some of those … so that’s probably one I’m most excited about. But there’s so many right now. 

LEE: Well, I think I need to let you actually state the name of the clinical note-taking product that you’re associated with. Would you like to do that? [LAUGHS] 

LUNGREN: [LAUGHS] Sure. Yeah, it’s called DAX Copilot [3]. And for the record, it is the fastest-growing copilot in the Microsoft ecosystem. We’re very proud of that. Five hundred institutions already are using it, and millions of notes have already been created with it. And the feedback has been tremendous.

LEE: So, you sort of referred to this a little bit, you know, this idea of AI being a second set of eyes. So, doctor makes some decisions in diagnosis or kind of working out potential treatments or medication decisions. And in the book, you know, we surmise that, well, AI might not replace the doctor doing those things. It could but might not. But AI could possibly reduce errors if doctors and nurses are making decisions by just looking at those decisions and just checking them out. Is that happening at all, and what do you see the future there? 

LUNGREN: Yeah, I would say, you know, that’s kind of the jagged edge of innovation, right, where sometimes the capability gets ahead of the ability to, you know, operationalize that. You know, part of that is just related to the systems. The evidence has been interesting on this. So, like, you know this, our colleague Eric Horvitz has been doing a lot of work in sort of looking at physician, physician with GPT-4, let’s say, and then GPT-4 alone for a whole variety of things. You know, we’ve been saying to the world for a long time, particularly in the narrow AI days, that AI plus human is better than either alone. We’re not really seeing that bear out really that well yet in some of the research.  

But it is a signal to me and to the use case you’re suggesting, which is that if we let this system, in the right way, kind of handle a lot of the safety-net aspects of what we do but then also potentially take on some of the things that maybe are not that challenging or at least somewhat simple.  

And of course, this is really an interesting use case in my world, in the vision world, which is that we know these models are multimodal, right. They can process images and text. And what does that look like for pathologists or radiologists, where we do have a certain percentage of the things we look at in a given day are normal, right? Or as close to normal as you can imagine. So is there a way to do that? And then also, by the way, have a safety net.  

And so I think that this is an extremely active area right now. I don’t think we’ve figured out exactly how to have the human and AI model interact in this space yet. But I know that there’s a lot of attempts at it right now. 

LEE: Yeah, I think, you know, this idea of a true copilot, you know, a true collaborator, you know, I think is still something that’s coming. I think we’ve had a couple of decades of people being trained to think of computers as question-answering machines. Ask a question, get an answer. Provide a document, get a summary. And so on.  

But the idea that something might actually be this second set of eyes just assisting you all day continuously, I think, is a new mode of interaction. And we haven’t quite figured that out.  

Now, in preparation for this podcast, Matt, you said that you actually used AI to assist you in getting ready. [LAUGHS] Would you like to share what you learned by doing that? 

LUNGREN: Yeah, it’s very funny. So, like, you may have heard this term coined by Ethan Mollick called the “secret cyborg,” (opens in new tab) which is sort of referring to the phenomena of folks using GPT, realizing it can actually help them a ton in all kinds of parts of their work, but not necessarily telling anybody that they’re using it, right.  

And so in a similar secret cyborgish way, I was like, “Well, listen, you know, I haven’t read your book in like a year. I recommend it to everybody. And [I need] just a refresher.” So what I did was I took your book, I put it into GPT-4, OK, and asked it to sort of talk about the predictions that you made.  

And then I took that and put it in the stronger reasoning model—in this case, the “deep research” that you may have just seen or heard of and the audience from OpenAI—and asked it to research all the current papers, you know, and blogs and whatever else and tell me like what was right, what was wrong in terms of the predictions. [LAUGHS]  

So it, actually, it was an incredible thing. It’s a, like, what, six or seven pages. It probably would have taken me two weeks, frankly, to do this amount of work.  

LEE: I’ll be looking forward to reading that in the New England Journal of Medicine shortly. 

LUNGREN: [LAUGHS] That’s right. Yeah, no, don’t, before this podcast comes out, I’ll submit it as an opinion piece. No. [LAUGHS] But, yeah, but I think on balance, incredibly insightful views. And I think part of that was, you know, your team that got together really had a lot of different angles on this. But, you know, and I think the only area that was, like, which I’ve observed as well, it’s just, man, this can do a lot for education.  

We haven’t seen … I don’t think we’re looking at this as a tutor. To your point, we’re kind of looking at it as a transactional in and out. But as we’ve seen in all kinds of data, both in low-, middle-income countries and even in Harvard, using this as a tutor can really accelerate your knowledge and in profound ways.  

And so that is probably one area where I think your prediction was maybe slightly even further ahead of the curve because I don’t think folks have really grokked that opportunity yet. 

LEE: Yeah, and for people who haven’t read the book, you know, the guess was that you might use this as a training aid if you’re an aspiring doctor. For example, you can ask GPT-4 to pretend to be a patient that presents a certain way and that you are the doctor that this patient has come to see. And so you have an interaction. And then when you say end of encounter, you ask GPT-4 to assess how well you did. And we thought that this might be a great training aid, and to your point, it seems not to have materialized.  

LUNGREN: There’s some sparks. You know, with, like, communication, end-of-life conversations that no physician loves to have, right. It’s very, very hard to train someone in those. I’ve seen some work done, but you’re right. It’s not quite hit mainstream yet. 

LEE: On the subject of things that we missed, one thing that you’ve been very, very involved in in the last several months has been in shipping products that are multimodal. So that was something I think that we missed completely. What is the current state of affairs for multimodal, you know, healthcare AI, medical AI? 

LUNGREN: Yeah, the way I like to explain it—and first of all, no fault to you, but this is not an area that, like, we were just so excited about the text use cases that I can’t fault you. But yeah, I mean, so if we look at healthcare, right, how we take care of patients today, as you know, the vast majority of the data in terms of just data itself is actually not in text, right. It’s going be in pathology and genomics and radiology, etc.  

And it seems like an opportunity here to watch this huge curve just goes straight up in the general reasoning and frankly medical competency and capabilities of the models that are coming and continue to come but then to see that it’s not as proficient for medical-specific imaging and video and, you know, other data types. And that gap is, kind of, what I describe as the multimodal medical AI gap.  

We’re probably in GPT-2 land, right, for this other modality types versus the, you know, we’re now at o3, who knows where we’re going to go. At least in our view, we can innovate in that space.  

How do we help bring those innovations to the broader community to close that gap and see some of these use cases really start to accelerate in the multimodal world?  

And I think we’ve taken a pretty good crack at that. A lot of that is credit to the innovative work. I mean, MSR [Microsoft Research] was two or three years ahead of everyone else on a lot of this. And so how do we package that up in a way that the community can actually access and use? And so, we took a lot of what your group had done in, let’s just say, radiology or pathology in particular, and say, “OK, well, let’s put this in an ecosystem of other models.” Other groups can participate in this, but let’s put it in a platform where maybe I’m really competent in radiology or pathology. How do I connect those things together? How do I bring the general reasoner knowledge into a multimodal use case?  

And I think that’s what we’ve done pretty well so far. We have a lot of work to do still, but this is very, very exciting. We’re seeing just such a ton of interest in building with the tools that we put out there. 

LEE: Well, I think how rapidly that’s advancing has been a surprise to me. So I think we’re running short on time. So two last questions to wrap up this conversation. The first one is, as we think ahead on AI in medicine, what do you think will be the biggest changes or make the biggest differences two years from now, five years from now, 10 years from now?

LUNGREN: This is really tough. OK. I think the two-year timeframe, I think we will have some autonomous agent-based workflows for a lot of the … what I would call undifferentiated heavy lifting in healthcare.  

And this is happening in, you know, the pharmaceutical industry, the payer … every aspect is sort of looking at their operations at a macro level: where are these big bureaucratic processes that largely involve text and where can we shrink those down and really kind of unlock a lot of our workforce to do things that might be more meaningful to the business? I think that’s my safe one.  

Going five years out, you know, I have a really difficult time grappling with this seemingly shrinking timeline to AGI [artificial general intelligence] that we hear from people who I would respect and certainly know more than me. And in that world, I think there’s only been one paper that I’ve seen that has attempted to say, what does that mean in healthcare (opens in new tab) when we have this?  

And the fact is, I actually don’t know. [LAUGHS] I wonder whether there’ll still be a gap in some modalities. Maybe there’ll be the ability to do new science, and all kinds of interesting things will come of that.  

But then if you go all the way to your 10-year, I do feel like we’re going to have systems that are acting autonomously in a variety of capacities, if I’m being honest.  

What I would like to see if I have any influence on some of this is, can we start to celebrate the closing of hospitals instead of opening them? Meaning that, can we actually start to address—at a personal, individual level—care? And maybe that’s outside the home, maybe that’s, you know, in a way that doesn’t have to use so many resources and, frankly, really be very reactive instead of proactive.  

I really want to see that. That’s been the vision of precision medicine for, geez, 20-plus years. I feel like we’re getting close to that being something we can really tackle. 

LEE: So, we talked about Geoff Hinton and his famous prediction that we would soon not have human radiologists. And of course, maybe he got the date wrong. So, let’s reset the date to 2028. So, Matt, do you think Geoff is right or wrong? 

LUNGREN: [LAUGHS] Yeah, so the way … I’m not going to dodge the question, but let me just answer this a different way.  

We have a clear line of sight to go from images to draft reports. That is unmistakable. And that’s now in 2025. How it will be implemented and what the implications of that will be, I think, will be heavily dependent on the health system or the incentive structure for where it’s deployed.  

So, if I’m trying to take a step back, back to my global health days, man, that can’t come fast enough. Because, you know, you have entire health systems, you know, in fact entire countries that have five, you know, medical imaging experts for the whole country, but they still need this to you know take care of patients.  

Zooming in on today’s crisis in the US, right, we have the burnout crisis just as much as the doctors who are seeing patients and write notes. We can’t keep up with the volume. In fact, we’re not training folks fast enough, so there is a push pull; there may be a flip to your point of autonomous reads across some segments of what we do.  

By 2028, I think that’s a reasonable expectation that we’ll have some form of that. Yes. 

LEE: I tend to agree, and I think things get reshaped, but it seems very likely that even far into the future we’ll have humans wanting to take care of other humans and be taken care of by humans.  

Matt, this has been a fantastic conversation, and, you know, I feel it’s always a personal privilege to have a chance to work with someone like you so keep it up. 

LUNGREN: Thank you so much, Peter. Thanks for having me. 

LEE: I’m always so impressed when I talk to Matt, and I feel lucky that we get a chance to work together here at Microsoft. You know, one of the things that always strikes me whenever I talk to him is just how disruptive generative AI has been to a business like Nuance. Nuance has had clinical note-taking as part of their product portfolio for a long, long time. And so, you know, when generative AI comes along, it’s not only an opportunity for them, but also a threat because in a sense, it opens up the possibility of almost anyone being able to make clinical note-taking capabilities into products.  

It’s really interesting how Matt’s product, DAX Copilot, which since the time that we had our conversation has expanded into a full healthcare workflow product called Dragon Copilot, has really taken off in the marketplace and how many new competing AI products have also hit the market, and all in just two years, because of generative AI.  

The other thing, you know, that I always think about is just how important it is for these kinds of systems to work together and especially how they integrate into the electronic health record systems. This is something that Carey, Zak, and I didn’t really realize fully when we wrote our book. But you know, when you talk to both Matt and Seth, of course, we see how important it is to have that integration.  

Finally, what a great example of yet another person who is both a surgeon and a tech geek. [LAUGHS] People sometimes think of healthcare as moving very slowly when it comes to new technology, but people like Matt are actually making it happen much more quickly than most people might expect.  

Well, anyway, as I mentioned, we also had a chance to talk to Seth Hain, and so here’s my conversation with Seth:

LEE: Seth, thank you so much for joining.  

SETH HAIN: Well, Peter, it’s such an exciting time to sit down and talk about this topic. So much has changed in the last two years. Thanks for inviting me.  

LEE: Yeah, in fact, I think in a way both of our lives have been upended in many ways by the emergence of AI. [LAUGHTER]  

The traditional listeners of the Microsoft Research Podcast, I think for the most part, aren’t steeped in the healthcare industry. And so maybe we can just start with two things. One is, what is Epic, really? And then two, what is your job? What does the senior vice president for R&D at Epic do every day? 

HAIN: Yeah, well, let’s start with that first question. So, what is Epic? Most people across the world experience Epic through something we call MyChart. They might use it to message their physician. They might use it to check the lab values after they’ve gotten a recent test. But it’s an app on their phone, right, for connecting in with their doctors and nurses and really making them part of the care team.  

But the software we create here at Epic goes beyond that. It’s what runs in the clinic, what runs at the bedside, in the back office to help facilitate those different pieces of care, from collecting vital information at the bedside to helping place orders if you’re coming in for an outpatient visit, maybe with a kiddo with an earache, and capturing that note and record of what happened during that encounter, all the way through back-office encounters, back-office information for interacting with payers as an example.  

And so, we provide a suite of software that health systems and increasingly a broader set of the healthcare ecosystem, like payers and specialty diagnostic groups, use to connect with that patient at the center around their care. 

And my job is to help our applications across the company take advantage of those latest pieces of technology to help improve the efficiency of folks like clinicians in the exam room when you go in for a visit. We’ll get into, I imagine, some use cases like ambient conversations, capturing that conversation in the exam room to help drive some of that documentation.  

But then providing that platform for those teams to build those and then strategize around what to create next to help both the physicians be efficient and also the health systems. But then ultimately continuing to use those tools to advance the science of medicine. 

LEE: Right. You know, one thing that I explain to fellow technologists is that I think today health records are almost entirely digital. I think the last figures I saw is well over 99% of all health records are digital.  

But in the year 2001, fewer than 15% of health records were digital. They were literally in folders on paper in storerooms, and if you’re old enough, you might even remember seeing those storerooms.  

So, it’s been quite a journey. Epic and Epic’s competitors—though I think Epic is really the most important company—have really moved the entire infrastructure of record keeping and other communications in healthcare to a digital foundation.  

And I think one thing we’ll get into, of course, one of the issues that has really become, I think, a problem for doctors and nurses is the kind of clerical or paperwork, record-keeping, burden. And for that reason, Epic and Epic systems end up being a real focus of attention. And so, we’ll get into that in a bit here.  

HAIN: And I think that hits, just to highlight it, on both sides. There is both the need to capture documentation; there’s also the challenge in reviewing it.  

LEE: Yes.  

HAIN: The average medical record these days is somewhere between the length of Fahrenheit 451 and To Kill a Mockingbird. [LAUGHTER] So there’s a fair amount of effort going in on that review side, as well. 

LEE: Yeah, indeed. So much to get into there. But I would like to talk about encounters with AI. So obviously, I think there are two eras here: before the emergence of ChatGPT and what we now call of as generative AI and afterwards. And so, let’s take the former.  

Of course, you’ve been thinking about machine learning and health data probably for decades. Do you have a memory of how you got into this? Why did you get an interest in data analytics and machine learning in the first place? 

HAIN: Well, my background, as you noted, is in mathematics before I came to Epic. And the sort of patterns and what could emerge were always part of what drove that. Having done development and kind of always been around computers all my life, it was a natural transition as I came here.  

And I started by really focusing on, how do we scale systems for the very largest organizations, making sure they are highly available and also highly responsive? Time is critical in these contexts in regards to rapidly getting information to doctors and nurses.  

And then really in the, say, in the 2010s, there started to be an emergence of capabilities from a storage and compute perspective where we could begin to build predictive analytics models. And these were models that were very focused, right. It predicted the likelihood somebody would show up for an appointment. It predicted the likelihood that somebody may fall during an inpatient stay, as an example.  

And I think a key learning during that time period was thinking through the full workflow. What information was available at that point in time, right? At the moment somebody walks into the ED [emergency department], you don’t have a full picture to predict the likelihood that they may deteriorate during an inpatient encounter.  

And in addition to what information was available was, what can you do about it? And a key part of that was how do we help get the right people in the right point in time at the bedside to make an assessment, right? It was a human-in-the-loop type of workflow where, for example, you would predict deterioration in advance and have a nurse come to the bedside or a physician come to the bedside to assess.  

And I think that combination of narrowly focused predictive models with an understanding that to have them make an impact you had to think through the full workflow of where a human would make a decision was a key piece. 

LEE: Obviously there is a positive human impact. And so, for sure, part of the thought process for these kinds of capabilities comes from that.  

But Epic is also a business, and you have to worry about, you know, what are doctors and clinics and healthcare systems willing to buy. And so how do you balance those two things, and do those two things ever come into conflict as you’re imagining what kinds of new capabilities and features and products to create? 

HAIN: Two, sort of, two aspects I think really come to mind. First off, generally speaking, we see analytics and AI as a part of the application. So, in that sense, it’s not something we license separately. We think that those insights and those pieces of data are part of what makes the application meaningful and impactful.  

At the scale that many of these health systems operate and the number of patients that they care for, as well as having tens of thousands of users in the system daily, one needs to think about the compute overhead … 

LEE: Yes. 

HAIN: … that these things cause. And so, in that regard, there is always a ROI assessment that is taking place to some degree around, what happens if this runs at full scale? And in a way, that really got accelerated as we went into the generative AI era.  

LEE: Right. OK. So, you mentioned generative AI. What was the first encounter, and what was that experience for you?

HAIN: So, in the winter of ’22 and into 2023, I started experimenting alongside you with what we at that time called DV3, or Davinci 3, and eventually became GPT-4. And immediately, a few things became obvious. The tool was highly general purpose. One was able to, in putting in a prompt, have it sort of convert into the framing and context of a particular clinical circumstance and reason around that context. But I think the other thing that started to come to bear in that context was there was a fair amount of latent knowledge inside of it that was very, very different than anything we’d seen before. And, you know, there’s some examples from the Sparks of AGI paper from Microsoft Research, where a series of objects end up getting stacked together in the optimal way to build height. Just given the list of objects, it seems to have a understanding of physical space that it intuited from the training processes we hadn’t seen anywhere. So that was an entirely new capability that programmers now had access to.  

LEE: Well in fact, you know, I think that winter of 2022, and we’ll get into this, one of your projects that you’ve been running for quite a few years is something called Cosmos (opens in new tab), which I find exceptionally interesting. And I was motivated to understand whether this type of technology could have an impact there.  

And so, I had to receive permission from both OpenAI and Microsoft to provide you with early access.  

When I did first show this technology to you, you must have had an emotional response, either skepticism or … I can’t imagine you just trusted, you know, trusted me to the extent of believing everything I was telling you. 

HAIN: I think there’s always a question of, what is it actually, right? It’s often easy to create demos. It’s often easy to show things in a narrow circumstance. And it takes getting your hands on it and really spending your 10,000 hours digging in and probing it in different ways to see just how general purpose it was.  

And so, the skepticism was really around, how applicable can this be broadly? And I think the second question—and we’re starting to see this play out now in some of the later models—was, is this just a language thing? Is it narrowly only focused on that? Or can we start to imagine other modalities really starting to factor into this? How will it impact basic sciences? Those sorts of things.

On a personal note, I mean, I had, at that point, now they’re now 14 and 12, two kids that I wondered, what did this mean for them? What is the right thing for them to be studying? And so I remember sleepless nights on that topic, as well. 

LEE: OK, so now you get early access to this technology; you’re able to do some experimentation. I think one of the things that impressed me is just less than four months later at the major health tech industry conference, HIMSS, which also happened timing-wise to take place just after the public disclosure of GPT-4, Epic showed off some early prototype applications of generative AI. And so, describe what those were, and how did you choose what to try to do there? 

HAIN: Yeah, and we were at that point, we actually had the very first users live on that prototype, on that early version.  

And the key thing we’d focused on—we started this development in very, very late December, January of 2023—was a problem that its origins really were during the pandemic.  

So, during the pandemic, we started to see patients increasingly messaging their providers, nurses, and clinicians through MyChart, that patient portal I mentioned with about 190 million folks on it. And as you can imagine, that was a great opportunity in the context of COVID to limit the amount of direct contact between providers and patients while still getting their questions answered.  

But what we found as we came out of the pandemic was that folks preferred it regardless. And that messaging volume had stayed very, very high and was a time-consuming effort for folks.  

And so, the first use case we came out with was a draft message in the context of the message from the patient and understanding of their medical history using that medical record that we talked about.  

And the nurse or physician using the tool had two options. They could either click to start with that draft and edit it and then hit send, or they could go back to the old workflow and start with a blank text box and write it from their own memory as they preferred.

And so that was that very first use case. There were many more that we had started from a development perspective, but, yeah, we had that rolling out right in March of 2023 there with the first folks. 

LEE: So, I know from our occasional discussions that some things worked very well. In fact, this is a real product now for Epic. And it seems to be really a very, very popular feature now. I know from talking to you that a lot of things have been harder. And so, I’d like to dive into that. As a developer, tech developer, you know, what’s been easy, what’s been hard, what’s in your mind still is left to do in terms of the development of AI? 

HAIN: Yeah. You know, the first thing that comes to mind sort of starting foundationally, and we hinted at this earlier in our conversation, was at that point in time, it was kind of per a message, rather compute-intensive to run these. And so, there were always trade-offs we were making in regards to how many pieces of information we would send into the model and how much would we request back out of it.  

The result of that was that while kind of theoretically or even from a research perspective, we could achieve certain outcomes that were quite advanced, one had to think about, where you make those trade-offs from a scalability perspective as you wanted to roll that out to lot of folks. So … 

LEE: Were you charging your customers more money for this feature? 

HAIN: Yeah, essentially the way that we handle that is there’s compute that’s required. As I mentioned, the feature is just part of our application. So, it’s just what they get with an upgrade.  

But that compute overhead is something that we needed to pass through to them. And so, it was something, particularly given both the staffing challenges, but also the margin pressures that health systems are feeling today, we wanted to be very cautious and careful about. 

LEE: And let’s put that on the stack because I do want to get into, from the selling perspective, that challenge and how you perceive health systems as a customer making those trade-offs. But let’s continue on the technical side here. 

HAIN: Yeah. On the technical side, it was a consideration, right. We needed to be thoughtful about how we used them. But going up a layer in the stack, at that time, there’s a lot of conversation in the industry around something called RAG, or retrieval-augmented generation.  

And the idea was, could you pull the relevant bits, the relevant pieces of the chart, into that prompt, that information you shared with the generative AI model, to be able to increase the usefulness of the draft that was being created? And that approach ended up proving and continues to be to some degree, although the techniques have greatly improved, somewhat brittle, right. You have a general-purpose technology that is drafting the response. 

But in many ways, you needed to, for a variety of pragmatic reasons, have somewhat brittle capability in regards to what you pulled into that approach. It tended to be pretty static. And I think this becomes one of the things that, looking forward, as these models have gotten a lot more efficient, we are and will continue to improve upon because, as you get a richer and richer amount of information into the model, it does a better job of responding.  

I think the third thing, and I think this is going to be something we’re going to continue to work through as an industry, was helping users understand and adapt to these circumstances. So many folks when they hear AI think, it will just magically do everything perfectly.  

And particularly early on with some of those challenges we’re talking about, it doesn’t. You know, if it’s helpful 85% of the time, that’s great, but it’s not going to be 100% of the time. And it’s interesting as we started, we do something we call immersion, where we always make sure that developers are right there elbow to elbow with the users of the software. 

And one of the things that I realized through that experience with some of the very early organizations like UCSD [UC San Diego] or University of Wisconsin here in Madison was that even when I’m responding to an email or a physician is responding to one of these messages from a patient, depending on the patient and depending on the person, they respond differently.  

In that context, there’s opportunity to continue to mimic that behavior as we go forward more deeply. And so, you learn a lot about, kind of, human behavior as you’re putting these use cases out into the world. 

LEE: So, you know, this increasing burden of electronic communications between doctors, nurses, and patients is centered in one part of Epic. I think that’s called your in-basket application, if I understand correctly.  

HAIN: That’s correct. 

LEE: But that also creates, I think, a reputational risk and challenge for Epic because as doctors feel overburdened by this and they’re feeling burnt out—and as we know, that’s a big issue—then they point to, you know, “Oh, I’m just stuck in this Epic system.”  

And I think a lot of the dissatisfaction about the day-to-day working lives of doctors and nurses then focuses on Epic. And so, to what extent do you see technologies like generative AI as, you know, a solution to that or contributing either positively or negatively to this? 

HAIN: You know, earlier I made the comment that in December, as we started to explore this technology, we realized there were a class of problems that now might have solutions that never did before.  

And as we’ve started to dig into those—and we now have about 150 different use cases that are under development, many of which are live across … we’ve got about 350 health systems using them—one of the things we’ve started to find is that physicians, nurses, and others start to react to saying it’s helping them move forward with their job.  

And examples of this, obviously the draft of the in-basket message response is one, but using ambient voice recognition as a kind of new input into the software so that when a patient and a physician sit down in the exam room, the physician can start a recording and that conversation then ends up getting translated or summarized, if you will, including using medical jargon, into the note in the framework that the physician would typically write.  

Another one of those circumstances where they then review it, don’t need to type it out from scratch, for example, …  

LEE: Right. 

HAIN: … and can quickly move forward.  

I think looking forward, you know, you brought up Cosmos earlier. It’s a suite of applications, but at its core is a dataset of about 300 million de-identified patients. And so using generative AI, we built research tools on top of it. And I bring that up because it’s a precursor of how that type of deep analytics can be put into context at the point of care. That’s what we see this technology more deeply enabling in the future. 

LEE: Yeah, when you are creating … so you said there are about 150 sort of integrations of generative AI going into different parts of Epic’s software products.  

When you are doing those developments and then you’re making a decision that something is going to get deployed, one thing that people might worry about is, well, these AI systems hallucinate. They have biases. There are unclear accountabilities, you know, maybe patient expectations.  

For example, if there’s a note drafted by AI that’s sent to a patient, does the patient have a right to know what was written by AI and what was written by the human doctor? So, can we run through how you have thought about those things?  

HAIN: I think one thing that is important context to set here for folks, and I think it’s often a point of confusion when I’m chatting with folks in public, is that their interaction with generative AI is typically through a chatbot, right. It’s something like ChatGPT or Bing or one of these other products where they’re essentially having a back-and-forth conversation. 

LEE: Right. 

HAIN: And that is a dramatically different experience than how we think it makes sense to embed into an enterprise set of applications.  

So, an example use case may be in the back office, there are folks that are coding encounters. So, when a patient comes in, right, they have the conversation with the doctor, the doctor documents it, that encounter needs to be billed for, and those folks in the back-office associate to that encounter a series of codes that provide information about how that billing should occur.

So, one of the things we did from a workflow perspective was add a selector pane to the screen that uses generative AI to suggest a likely code. Now, this suggestion runs the risk of hallucination. So, the question is, how do you build into the workflow additional checks that can help the user do that?  

And so in this context, we always include a citation back to the part of the medical record that justifies or supports that code. So quickly on hover, the user can see, does this make sense before selecting it? And it’s those types of workflow pieces that we think are critical to using this technology as an aid to helping people make decisions faster, right. It’s similar to drafting documentation that we talked about earlier.  

And it’s interesting because there’s a series of patterns that are … going back to the AI Revolution book you folks wrote two years ago. Some of these are really highlighted there, right. This idea of things like a universal translator is a common pattern that we ended up applying across the applications. And in my mind, translation, this may sound a little bit strange, but summarization is an example of translating a very long series of information in a medical record into the context that an ED physician might care about, where they have three or four minutes to quick review that very long chart.  

And so, in that perspective, and back to your earlier comment, we added the summary into the workflow but always made sure that the full medical record was available to that user, as well. So, a lot of what we’ve done over the last couple of years has been to create a series of repeatable techniques in regards to both how to build the backend use cases, where to pull the information, feed it into the generative AI models.  

But then I think more importantly are the user experience design patterns to help mitigate those risks you talked about and to maintain consistency across the integrated suite of applications of how those are deployed.  

LEE: You might remember from our book, we had a whole chapter on reducing paperwork, and I think that’s been a lot of what we’ve been talking about. I want to get beyond that, but before transitioning, let’s get some numbers.  

So, you talked about messages drafted to patients, to be sent to patients. So, give a sense of the volume of what’s happening right now. 

HAIN: Oh, we are seeing across the 300 and, I think it’s, 48 health systems that are now using generative AI—and to be clear, we have about 500 health systems we have the privilege of working with, each with many, many hospitals—there are tens of thousands of physicians and nurses using the software. That includes drafting million-plus, for example, notes a month at this point, as well as helping to generate in a similar ballpark that number of responses to patients.  

The thing I’m increasingly excited about is the broader set of use cases that we’re seeing folks starting to deploy now. One of my favorites has been … it’s natural that as part of, for example, a radiology workflow, in studying that image, the radiologist made note that it would be worth double checking, say in six to eight months, that the patient have this area scanned of their chest. Something looks a little bit fishy there, but there’s not … 

LEE: There’s not a definitive finding yet. 

HAIN: … there’s not a definitive finding at that point. Part of that workflow is that the patient’s physician place an order for that in the future. And so, we’re using generative AI to note that back to the physician. And with one click, allow them to place that order, helping that patient get better care.  

That’s one example of dozens of use cases that are now live, both to help improve the care patients are getting but also help the workforce. So going back to the translation-summarization example, a nurse at the end of their shift needs to write up a summary of that shift for the next nurse for each … 

LEE: Right. 

HAIN: … each patient that they care for. Well, they’ve been documenting information in the chart over those eight or 12 hours, right.  

LEE: Yep, yep. 

HAIN: So, we can use that information to quickly draft that end-of-shift note for the nurse. They can verify it with those citations we talked about and make any additions or edits that they need and then complete their end of day far more efficiently.  

LEE: Right. OK. So now let’s get to Cosmos, which has been one of these projects that I think has been your baby for many years and has been something that has had a profound impact on my thinking about possibilities. So first off, what is Cosmos? 

HAIN: Well, just as an aside, I appreciate the thoughtful comments. There is a whole team of folks here that are really driving these projects forward. And a large part of that has been, as you brought up, both Cosmos as a foundational capability but then beginning to integrate it into applications. And that’s what those folks spend time on.  

Cosmos is this effort across hundreds of health systems that we have the privilege of working with to build out a de-identified dataset with today—and it climbs every day—but 300 million unique patient records in it.  

And one of the interesting things about that structure is that, for example, if I end up in a hospital in Seattle and have that encounter documented at a health system in Seattle, I still—a de-identified version of me—still only shows up once in Cosmos, stitching together both my information from here in Madison, Wisconsin, where Epic is at, with that extra data from Seattle. The result is these 300 million unique longitudinal records that have a deep history associated with them.  

LEE: And just to be clear, a patient record might have hundreds or even thousands of individual, I guess what you would call, clinical records or elements. 

HAIN: That’s exactly right. It’s the breadth of information from orders and allergies and blood pressures collected, for example, in an outpatient setting to cancer staging information that might have come through as part of an oncology visit. And it’s coming from a variety of sources. We exchange information about 10 million times a day between different health systems. And that full picture is available within Cosmos in that way of the patient. 

LEE: So now why? Why Cosmos? 

HAIN: Why Cosmos? Well, the real ultimate aim is to put a deeply informed in-context perspective at the point of care. So, as a patient, if I’m in the exam room, it’s helpful for the physician and me to know what have similar patients like me experienced in this context. What was the result of that line of treatment, for example? 

Or as a doctor, if I’m looking and working through a relatively rare or strange case to me, I might be able to connect with—this as an example workflow we built called Look-Alikes—with another physician who has seen similar patients or within the workflow see a list of likely diagnoses based on patients that have been in a similar context. And so, the design of Cosmos is to put those insights into the point of care in the context of the patient.  

To facilitate those steps there, the first phase was building out a set of research tooling. So, we see dozens of papers a year being published by the health systems that we work with. Those that participate in Cosmos have access to it to do research on it. And so they use both a series of analytical and data science tools to do that analysis and then publish research. So, building up trust that way.  

LEE: The examples you gave are, like with Look-Alikes, it’s very easy, I think, for people outside of the healthcare world to imagine how that could be useful. So now why is GPT-4 or any generative AI relevant to this? 

HAIN: Well, so a couple of different pieces, right. Earlier we talked about—and I think this is the most important—how generative AI is able to cast things into a specific context. And so, in that way, we can use these tools to help both identify a cohort of patients similar to you when you’re in the exam room. And then also help present that information back in a way that relates to other research and understandings from medical literature to understand what are those likely outcomes.  

I think more broadly, these tools and generative AI techniques in the transformer architecture envision a deeper understanding of sequences of events, sequences of words. And that starts to open up broader questions about what can really be understood about patterns and sequences of events in a patient’s journey.  

Which if you didn’t know, the name Epic, just like a great long nation’s journey is told through an epic story, is a patient’s story. So that’s where it came from. 

LEE: So, we’re running up against our time together. And I always like to end with a more provocative question.  

HAIN: Certainly. 

LEE: And for you, I wanted to raise a question that I think we had asked ourselves in the very earliest days that we were sharing Davinci 3, what we now know of as GPT-4, with each other, which is, is there a world in the future because of AI where we don’t need electronic health records anymore? Is there a world in the future without EHR? 

HAIN: I think it depends on how you define EHR. I see a world coming where we need to manage a hybrid workforce, where there is a combination of humans and something folks are sometimes calling agents working in concert together to care for more and more of our … of the country and of the world. And there is and will need to be a series of tools to help orchestrate that hybrid workforce. And I think things like EHRs will transform into helping that operate … be operationally successful.  

But as a patient, I think there’s a very different opportunity that starts to be presented. And we’ve talked about kind of understanding things deeply in context. There’s also a real acceleration happening in science right now. And the possibility of bringing that second- and third-order effects of generative AI to the point of care, be that through the real-world evidence we were talking about with Cosmos or maybe personalized therapies that really are well matched to that individual. These generative AI techniques open the door for that, as well as the full lifecycle of managing that from a healthcare perspective all the way through monitoring after the fact.  

And so, I think we’ll still be recording people’s stories. Their stories are relevant to them, and they can help inform the bigger picture. But I think the real question is, how do you put those in a broader context? And these tools open the door for a lot more. 

LEE: Well, that’s really a great vision for the future.  

Seth, I always really learn so much talking to you, and thank you so much for this great chat. 

HAIN: Thank you for inviting me.   

LEE: I see Seth as someone on the very leading frontier of bringing generative AI to the clinic and into the healthcare back office and at the full scale of our massive healthcare system. It’s always impressive to me how thoughtful Seth has had to be about how to deploy generative AI into a clinical setting.  

And, you know, one thing that sticks out—and he made such a point of this—is, you know, generative AI in the clinical setting isn’t just a chatbot. They’ve had to really think of other ways that will guarantee that the human stays in the loop. And that’s of course exactly what Carey, Zak, and I had predicted in our book. In fact, we even had a full chapter of our book entitled “Trust but Verify,” which really spoke to the need in medicine to always have a human being directly involved in overseeing the process of healthcare delivery. 

One technical point that Carey, Zak, and I completely missed, on the other hand, in our book, was the idea of something that Seth brought up called RAG, which is retrieval-augmented generation. That’s the idea of giving AI access to a database of information and allowing it to use that database as it constructs its answers. And we heard from Seth how fundamental RAG is to a lot of the use cases that Epic is deploying. 

And finally, I continue to find Seth’s project called Cosmos to be a source of inspiration, and I’ve continued to urge every healthcare organization that has been collecting data to consider following a similar path. 

In our book, we spent a great deal of time focusing on the possibility that AI might be able to reduce or even eliminate a lot of the clerical drudgery that currently exists in the delivery of healthcare. We even had a chapter entitled “The Paperwork Shredder.” And we heard from both Matt and Seth that that has indeed been the early focus of their work.  

But we also saw in our book the possibility that AI could provide diagnoses, propose treatment options, be a second set of eyes to reduce medical errors, and in the research lab be a research assistant. And here in Epic’s Cosmos, we are seeing just the early glimpses that perhaps generative AI can actually provide new research possibilities in addition to assistance in clinical decision making and problem solving. On the other hand, that still seems to be for the most part in our future rather than something that’s happening at any scale today. 

But looking ahead to the future, we can still see the potential of AI helping connect healthcare delivery experiences to the advancement of medical knowledge. As Seth would say, the ability to connect bedside to the back office to the bench. That’s a pretty wonderful future that will take a lot of work and tech breakthroughs to make it real. But the fact that we now have a credible chance of making that dream happen for real, I think that’s pretty wonderful. 

I’d like to say thank you again to Matt and Seth for sharing their experiences and insights. And to our listeners, thank you for joining us. We have some really great conversations planned for the coming episodes, including a look at how patients are using generative AI for their own healthcare, as well as an episode on the laws, norms, and ethics developing around AI and health, and more. We hope you’ll continue to tune in.

Until next time.