Speaker 1: Welcome to tech Stuff. This is the inside View. I'm
Speaker 1: os Vloschen here with Cara Price.
Speaker 2: Hello, so as I'm very curious to know more about
Speaker 2: the story you've brought me this week, since it's a
Speaker 2: topic we discussed a lot on this podcast.
Speaker 1: Yes, so today I've got a story about AI in healthcare,
Speaker 1: specifically AI and diagnosis. I spoke with doctor Matthew Lungren,
Speaker 1: who is the chief Scientific officer for Microsoft Health and
Speaker 1: Life Sciences, about this blog post that Microsoft recently published
Speaker 1: with the title the Path to Medical Superintelligence.
Speaker 2: Do I want to know what medical superintelligence is? It's
Speaker 2: more big than just regular intelligence. But I actually heard
Speaker 2: about this study. It was everywhere, and if I remember correctly,
Speaker 2: it was that the AI were better at diagnosing than doctors.
Speaker 1: Right, Yeah, that's right, In fact, four times better. There
Speaker 1: was a headline in Time magazine which really says it all.
Speaker 1: Microsoft's AI is better than doctors are diagnosing disease. Special
Speaker 1: shout out here to Elliot Fishman, who's our old friend.
Speaker 1: He's a professor of radiology at Johns Hopkins and he
Speaker 1: runs this fascinating email group that discusses new developments in AI.
Speaker 1: Matthew Lunger and I are both members of this group,
Speaker 1: and Matthew is also one of the authors of the study.
Speaker 2: What kind of doctor is Doctor Lungren?
Speaker 1: Like Elliott Fishman our friend, he's a radiologist by training
Speaker 1: and has a public health background. He was hired at
Speaker 1: Stanford where he started using machine learning to analyze large
Speaker 1: data sets. Here's Matthew.
Speaker 3: Eventually my lab grew into a very large AI center
Speaker 3: at Stanford, which bridged the computer science department in the
Speaker 3: medical school and kind of saw translation of newest techniques
Speaker 3: into healthcare applications accelerate. Taking that work further, I went
Speaker 3: to Microsoft on sabbatical at Microsoft Research and realized that
Speaker 3: a very similar opportunity was there in big tech if
Speaker 3: you could start to connect the latest technology to problems
Speaker 3: in healthcare. And so that's how I came to be here,
Speaker 3: and that's kind of what I still do all day.
Speaker 1: And Matthew is also one of the authors of the
Speaker 1: Microsoft study.
Speaker 3: I believe that the human expert plus these expert systems
Speaker 3: together will ultimately deliver better care.
Speaker 4: No matter what.
Speaker 3: Profession you're in, there's always a gray haired person that has,
Speaker 3: you know, in some sense, seen it all and kind
Speaker 3: of compressed that into their brain and their pattern matching
Speaker 3: in a way that is just faster than folks that
Speaker 3: don't have as much experience. And that's true anywhere, but
Speaker 3: certainly in medicine, right. I think that the assistance or
Speaker 3: ability of AI to now sort of connect dots in
Speaker 3: ways that maybe can achieve that wisdom or that experience
Speaker 3: and bring that to the surface.
Speaker 4: It's kind of an unprecedented time.
Speaker 1: The only exceptional performance I four times better than human doctors.
Speaker 1: One of the things I found most interesting about the
Speaker 1: study was that it wasn't just one single AI model
Speaker 1: doing a diagnosis. It was a whole team of AI
Speaker 1: models that were able to talk to each other in
Speaker 1: order to count with hypotheses, order tests, and ultimately count
Speaker 1: with a diagnosis.
Speaker 2: So multiple AI models seems a little bit unfair.
Speaker 1: Yes, and in fact we talked about this. The doctors
Speaker 1: in the study were not allowed to call specialists to
Speaker 1: help them with their diagnosis, but the ais were allowed
Speaker 1: to talk to each other. So doctors are not going
Speaker 1: to be made obsolete anytime soon.
Speaker 2: Well good, because I have a physical coming up and
Speaker 2: I don't need four AI models being like, well, this
Speaker 2: girl got real big this year.
Speaker 1: Now, as you and I already know, people are already
Speaker 1: using AI regularly to diagnose themselves. In fact, I think
Speaker 1: more than ten percent of the overall CHATCHBT traffic is
Speaker 1: around medical stuff. This is not always music to the
Speaker 1: ear of doctors, so it was interesting to look at
Speaker 1: an example where this is actually an AI build built
Speaker 1: for doctors and to work with doctors rather than patient facing.
Speaker 1: And the other interesting thing for me, which we talk
Speaker 1: about with Lunger, which we'll get to, is how this
Speaker 1: idea of multiple ais talking to each other can simulate
Speaker 1: the experience of the best hospital systems in the US
Speaker 1: for people who otherwise might not have access to these
Speaker 1: panels and experts.
Speaker 2: I can't wait to hear what you learned from him.
Speaker 1: Well, here's the rest of my conversation with doctor Matthew Lungren.
Speaker 1: So you're a trained doctor, and I want to start
Speaker 1: with the basics, which is diagnosis. I'm not sure when
Speaker 1: the last time you made a diagnosis on a patient was,
Speaker 1: but I'd love to hear from you as a doctor.
Speaker 1: What is the process of diagnosis?
Speaker 4: Yeah, I mean it depends quite a bit on the specialty.
Speaker 3: But as most people know, the classic image of a physician,
Speaker 3: right is to speak with the.
Speaker 4: Patient, kind of do a Sherlock Holmes kind of thing.
Speaker 3: Everyone's seen the shows like House and Things are kind
Speaker 3: of sensationalized sort of the approach.
Speaker 4: But really there's a lot of unknowns that you have
Speaker 4: to tease out.
Speaker 2: Right.
Speaker 3: You have to interview the page, you have to obviously
Speaker 3: interpret labs and other information, and you have to start
Speaker 3: to narrow things down and order appropriate tests. Try not
Speaker 3: to chase too many what we call the zebras, but
Speaker 3: keep those in mind in case you're dealing with one, and.
Speaker 1: The zebra would be the classic House episode.
Speaker 3: Right, yeah, right, Well every House episode is a zebra,
Speaker 3: which actually has some relationship to the study we're going
Speaker 3: to talk about today. But in general, it's more common
Speaker 3: to have an uncommon presentation of a common disease than
Speaker 3: in a common presentation of an uncommon disease, if that
Speaker 3: makes sense.
Speaker 1: Right, right, right, And this kind of relationship between AI
Speaker 1: and doctors has been going on for a few years.
Speaker 1: I remember reading a great piece in the Niyoka about
Speaker 1: how one of the challenges for AI was that the
Speaker 1: best doctors can't actually tell you in words why they're
Speaker 1: good at making diagnoses.
Speaker 4: That's right. It's interesting.
Speaker 3: I think there are things that humans have, many cotton
Speaker 3: adiases that are well undo and I think you know,
Speaker 3: keeping that in check while also trying to leverage the
Speaker 3: information in front of you not be affected by the
Speaker 3: case you just saw or something you just heard at
Speaker 3: a conference, or an error that you experienced years ago
Speaker 3: that's still impacting the way that you think about diagnoses.
Speaker 3: And I think those biases have been well published and
Speaker 3: discussed at nauseum in healthcare, but we're kind of dealing
Speaker 3: with this new human plus AI dance.
Speaker 1: That's fascinating. Yeah. I mean I actually slipped and fell
Speaker 1: down a few stairs at the weekend and bashed my
Speaker 1: head slightly on one of the stairs, and then didn't
Speaker 1: feel very well, and I was like, I wonder if
Speaker 1: I could be concussed. So I did a selfie and
Speaker 1: sent it to check GPT and it said my eyes
Speaker 1: look fine. So I actually, if I'd been more wired,
Speaker 1: I would have gone to the doctor. But there's a
Speaker 1: kind of a duck side to that as well.
Speaker 3: Yeah, I mean I think it sounds like you did okay,
Speaker 3: But I would say that the old saying in healthcare
Speaker 3: during the particularly the rise of the Internet, right, which
Speaker 3: is kind of the other similar kind of technology logic
Speaker 3: advancement that impacted healthcare. We used to say to our patients,
Speaker 3: you know, your Google search does not replace our medical degree, right,
Speaker 3: And that wasn't meant to be a condescending but it
Speaker 3: was just sort of like we had to sort of
Speaker 3: pull them back from the abyss of going down a
Speaker 3: rabbit hole and every ache and pain was immediately terminal cancer, right,
Speaker 3: that kind of But today it's different. It sort of
Speaker 3: reference the experience you just mentioned that's happening everywhere. In fact,
Speaker 3: the recent open Ai launch of GPD five, they spent
Speaker 3: fifteen minutes talking with a patient who went through a
Speaker 3: very difficult battle with cancer and worked with the model
Speaker 3: herself and was able to have very complex medical jard
Speaker 3: and explain to her in plain English, was able to
Speaker 3: help her with questions to ask the position. And as
Speaker 3: someone who still practices and sees patients today, I have
Speaker 3: to say my patients are better informed than maybe ever
Speaker 3: and it's kind of changing the bar with this classic
Speaker 3: information asymmetry problem where the patient has to kind of
Speaker 3: keep up up with the technical speak and all the
Speaker 3: information that we spend decades learning.
Speaker 4: It feels like there's almost a better playing field.
Speaker 3: So I can have this conversation with my patient almost
Speaker 3: at a peer level, is right, and then we can
Speaker 3: go through the care journey together. I'm extremely excited about
Speaker 3: that prospect.
Speaker 1: Taking a couple of steps back, I mean, you mentioned
Speaker 1: you've been in and around this since twenty twelve, twenty thirteen.
Speaker 1: Why do people want to use AI medicine.
Speaker 3: Well, it's an incredibly challenging discipline and it has only
Speaker 3: become more so maybe in the last ten or fifteen years.
Speaker 3: One of the things that is going on is that
Speaker 3: information is doubling roughly every ninety days medical information. That
Speaker 3: trend has been going on for a really long time.
Speaker 3: And what does publication of papers, publication of papers, new therapies,
Speaker 3: new guidelines, all these things keep stacking up, right, And
Speaker 3: so just because you've been through medical school and training, right,
Speaker 3: we have lots of systems in place to help us
Speaker 3: continue our education. But really the reaction to that has
Speaker 3: been to sub in some cases sub sub specialize. So
Speaker 3: to give you an example, I am a diagnostic radiologist,
Speaker 3: so that's the bigger specialty, and then I specialize in
Speaker 3: interventional radiology, which is an image guid to procedures basically,
Speaker 3: and then I am further specialized in pediatric version of that.
Speaker 3: So that's like a Russian nesting doll of specialties. And
Speaker 3: you see that throughout healthcare. And that is partly due
Speaker 3: to the complexity of care that's required for some patients,
Speaker 3: but also it's due to the information tidle wave and
Speaker 3: being able to hold all that in a human mind
Speaker 3: right with all of our limitations, and so AI, I
Speaker 3: think at least the work that we've been doing here
Speaker 3: is starting to provide a counter narrative to needing to
Speaker 3: be sub subspecialized in order to be able to manage
Speaker 3: information and take really good care of your patients across
Speaker 3: a wide variety of complex diagnoses. And I think that
Speaker 3: that's really where the excitement is. I think right now
Speaker 3: is can I use this system to augment my ability
Speaker 3: to care for PAYP.
Speaker 1: And why isn't AI more ubiquitous in medicine? And what
Speaker 1: has been integration challenge up until now, Well.
Speaker 3: There's a whole podcast just on that odds, I would say,
Speaker 3: but the short version is that we have been an
Speaker 3: incredibly skeptical discipline it's skeptical of new technology and at
Speaker 3: the same time extraordinarily risk averse for good reason, right,
Speaker 3: we require significant evidence, right to change the way we practice.
Speaker 3: We have you know, as you know, clinical trials take
Speaker 3: years and years, and some still fail, actually many fail,
Speaker 3: and we accept that as the system that keeps our
Speaker 3: patients safe and keeps us on the cutting edge. I
Speaker 3: think in terms of just the technical mechanics of adoption,
Speaker 3: we have a very rigid system in the software two
Speaker 3: world that is changing. What's so again, what's so exciting
Speaker 3: about this is that again any physician can pull out
Speaker 3: their cell phone and interact with this cutting edge AI
Speaker 3: without needing to have you know, three four year long
Speaker 3: cycles of integration with software. Right, and it's just the
Speaker 3: early days, but as of the trends that we're saying,
Speaker 3: just to.
Speaker 1: Take a step back, I guess the classic model of
Speaker 1: measuring AI performance versus doctor performance was to present a
Speaker 1: hard problem or a hard diagnostic conundrum and ask for
Speaker 1: an answer and measure answer versus answer. How is that
Speaker 1: different to what you've done?
Speaker 4: Yeah, well it's even less precise than that.
Speaker 3: So that the way up until now, at least for
Speaker 3: large language models, when people talk about they have medical capabilities,
Speaker 3: they were actually using medical examination questions.
Speaker 4: So there's a question stem and then there's a multiple
Speaker 4: choice answer.
Speaker 3: That's not medicine, but it is how we you know,
Speaker 3: qualify our humans, right, human physicians to be granted a
Speaker 3: medical license, so that we think we kind of use
Speaker 3: that for a long time as a as a surrogate
Speaker 3: or a bell weather, But it wasn't.
Speaker 1: Could it pause a test to be a doctor rather
Speaker 1: than could it actually be effective at acting as a doctor.
Speaker 3: That's interesting, right, And we were able to show very
Speaker 3: early on with GPD four that these models outperform positions
Speaker 3: on these multiple choice tests. But there's all kinds of
Speaker 3: caveats there. Is that really medicine? Has it seen some
Speaker 3: of that data and it's training assuredly?
Speaker 4: Yes? Right? And is that useful?
Speaker 3: I think those questions came up now in practice, it's
Speaker 3: estimated that ten to twenty percent of AI interactions with
Speaker 3: these common chatbots like GPT are around a medical use case.
Speaker 3: So we know that there's someone is getting value out
Speaker 3: of that somewhere, right, and we see it with our
Speaker 3: own eyes. So how do we bridge the gap to
Speaker 3: something a slightly more realistic in terms of not giving
Speaker 3: you all the information up front, just like we would
Speaker 3: in real healthcare. One of the principal thoughts around the
Speaker 3: study was is there a way to take advantage of
Speaker 3: the incredible capabilities that these models have in medical diagnosis.
Speaker 4: And knowledge but also push it a bit further.
Speaker 3: And not have it kind of just be a question
Speaker 3: answering machine. And so we thought, can we kind of
Speaker 3: have several versions of the model kind of act as
Speaker 3: different humans or this is that idea of an agent,
Speaker 3: and give them jobs. One job is to look at
Speaker 3: the economics of the tests that you're trying to order.
Speaker 3: One is to question your next decision point. So the
Speaker 3: information isn't just in and out with one model, it's
Speaker 3: actually in and out through a system of models. And
Speaker 3: we showed that no matter what model you use, whether
Speaker 3: it's Google's model, whether it's open the Eyes model, whether
Speaker 3: it's an open source model, it improves that diagnostic capability
Speaker 3: on these extraordinarily challenging diagnostic tests.
Speaker 1: So you had ten co authors on this study, and
Speaker 1: you know, as we talked about when it was released,
Speaker 1: took the world by storm, and so, I mean, how
Speaker 1: did you go about designing the study and what was
Speaker 1: the hypothesis and what have you found?
Speaker 3: So this was a cross Microsoft collaboration, but harsh and Noori,
Speaker 3: who is the lead on this, really wanted to say,
Speaker 3: you know, we have a lot of evidence that these
Speaker 3: models perform well for these standardized tests, and then we
Speaker 3: see the real world situation where that's not how people present.
Speaker 3: They don't show up with hey, these are all my tests,
Speaker 3: these are all my problems, and these are the four
Speaker 3: choices of what I may have right. And then taking
Speaker 3: what are essentially some of the most difficult questions out
Speaker 3: of New England Journal and structuring them in a way
Speaker 3: that requires a model to ask for more information or
Speaker 3: order tests, just.
Speaker 4: Like a physician would.
Speaker 3: The hypothesis was that that would be interesting and of itself,
Speaker 3: but then what if we also put humans through that
Speaker 3: same system. In other words, here's the first step headache, Okay,
Speaker 3: what do you do next?
Speaker 1: Well?
Speaker 4: Do I need to ask more questions? Do I need
Speaker 4: to order a test, et cetera, et cetera.
Speaker 3: One of the really brilliant outcomes here was by having
Speaker 3: that system of agents as opposed to just the single model,
Speaker 3: allowed us to have a more realistic understanding of the capabilities.
Speaker 3: In other words, if I wanted to know the answer,
Speaker 3: and I'm a chatbot, my answer could be, let's order
Speaker 3: every single test that there is, and that would probably
Speaker 3: get you the right answer.
Speaker 4: Is that feasible?
Speaker 2: No?
Speaker 4: Right?
Speaker 2: Ye?
Speaker 3: So forcing it to think about resources cost of the
Speaker 3: care actually found a very interesting what we would call
Speaker 3: the pride or frontier of capability underconstrained resources. So they
Speaker 3: were actually getting to an incredible diagnoses very very accurately,
Speaker 3: but also cost efficiently, and that was really one of
Speaker 3: the biggest takeaways from this work.
Speaker 1: Can you just to make it more concrete for our listeners,
Speaker 1: can you kind of set up one of these cases
Speaker 1: as though an episode of House Dare I say, and
Speaker 1: then what the human doctors did and what the AI
Speaker 1: agents did, and then how you compare that performance.
Speaker 3: Let's just say it was someone that had easy bleeding
Speaker 3: that unexpected. They were brushing their teeth and they started
Speaker 3: bleeding and it was kind of unusual, and they noticed
Speaker 3: that they were getting a lot of bruising, and there's
Speaker 3: just a certain battery of tests. I think that was
Speaker 3: pretty comparable on both sides in terms of what they ordered.
Speaker 3: But taking continued to.
Speaker 1: Be what the AI ordered and what than human doctors.
Speaker 4: Are human and AI pretty much right.
Speaker 3: So the first few steps, I think a lot there
Speaker 3: was a lot of similarity, which is expected. Where we
Speaker 3: started to see early diversions was because of that agent setup.
Speaker 3: Humans did kind of jump to more advanced tests more quickly,
Speaker 3: more expensive tests, and that was interesting because the models
Speaker 3: were able to kind of get to the next step
Speaker 3: with a battery of less expensive tests. So we thought
Speaker 3: that was a kind of an interesting starting to see
Speaker 3: some divergence. And then, to be fair to the humans,
Speaker 3: they're still kind of handcuffed. In other words, they're just
Speaker 3: getting text feedback as they're interacting with the system, whereas
Speaker 3: when I'm with a patient, I'm seeing them, I'm able
Speaker 3: to kind of take some cues, I'm able to examine them.
Speaker 3: So there was some limitations there, but then the less
Speaker 3: once it got to the stage where you had a
Speaker 3: differential diagnosis, so a list of likely things, more often
Speaker 3: than not, the model was ranking them in a much
Speaker 3: more data driven order that ultimately led to the correct
Speaker 3: diagnosis much more quickly. Whereas you know, as us you
Speaker 3: would with humans, with these limitations, you're kind of going
Speaker 3: in some rabbit holes, you're maybe not ordering them in
Speaker 3: the best order, and so you're kind of going down
Speaker 3: other paths that end up increasing the time or expense
Speaker 3: or potentially leading to the rown diagnosis.
Speaker 2: After the break, how the multi agent system the diagnostic
Speaker 2: orchestrator actually works stay with us.
Speaker 1: I put the study through chet GPT describe the diagnostic
Speaker 1: orchestrator as like a virtual team of five doctors, each
Speaker 1: with a different role. One less possible illnesses, one chooses
Speaker 1: the best tests, one plays devil's advocate, one watches the budget,
Speaker 1: and one checks the quality of everything. The team talks
Speaker 1: it out step by set, but decides what to do next.
Speaker 1: Is that is that a fair summary? That's exactly right?
Speaker 1: And you can have infinite numbers of those agents.
Speaker 3: I think these five were just kind of a scratching
Speaker 3: the surface of what's possible. I will say just quickly
Speaker 3: that I was incredibly happy to see that the curmudgeon
Speaker 3: agent we called it, or the Devil's advocate agent was
Speaker 3: helpful because you get into these group things situations, and
Speaker 3: it's kind of fun to watch a model argue with
Speaker 3: other models about some of the decisions being made in
Speaker 3: questioning the steps. So where the models fall short today
Speaker 3: is outside of the text domain. And what I mean
Speaker 3: by that is models are incredibly good at understanding medical
Speaker 3: concepts as their communicated in text form, but when you
Speaker 3: get into the images and genomics and waveforms and all
Speaker 3: the other types of ways that we take care of
Speaker 3: our patients, the models are vastly underperforming humans. And a
Speaker 3: good example of that is if I needed to look
Speaker 3: at a chest sexuray in one of these diagnostic steps
Speaker 3: and the model had to interpret the chess sector, it
Speaker 3: couldn't read the report actually had to look at the image,
Speaker 3: it would fall short and fail nine times out of ten.
Speaker 3: So we know that that's a significant gap. But on
Speaker 3: the other hand, most healthcare right eighty percent of physician
Speaker 3: or patients interaction with their healthcare systems involve some kind
Speaker 3: of other information like a ECG or a biopsy path
Speaker 3: slide right or a MRI for example. So I'm hoping
Speaker 3: to see agents that have those competencies included into the mix,
Speaker 3: or we can start to really get to a place
Speaker 3: where the diagnostic environment meets how we're testing the systems.
Speaker 1: There was a study last year which I was fascinated by.
Speaker 1: Wish is that AI diagnosis in this study was better
Speaker 1: than human plus AI. In other words, I was a study,
Speaker 1: and you would assume, or you would hope, that a
Speaker 1: doctor using AI would be better than just an AI
Speaker 1: diagnosis alone. But in fact, the human plus AI model
Speaker 1: was worse than the pure AI model. And one of
Speaker 1: the conclusions from this was maybe that the doctors what
Speaker 1: didn't want to listen to what AI was telling them.
Speaker 1: But I mean, did you see that study and did
Speaker 1: it give you pause?
Speaker 3: For more than a decade we've been kind of dealing
Speaker 3: with this unexpected result. This goes all again, all the
Speaker 3: way back to the earliest days of applying at least
Speaker 3: some of the powerful deep learning systems in healthcare, we
Speaker 3: have consistently seen that, in other words, in whatever set
Speaker 3: up the AI, if you just leave it alone, typically
Speaker 3: does better than the human plus THEI or.
Speaker 4: The human alone.
Speaker 3: Now is that a indictment on the human ability or
Speaker 3: is that more of a Have we set this up
Speaker 3: in a way that either doesn't favor the real world,
Speaker 3: or have we not figured out the ideal human computer
Speaker 3: interaction or how we should be What task should we
Speaker 3: be offloading to the system versus the task that we
Speaker 3: should be collaborating with the system on I think that's
Speaker 3: really where the exploration is that I'm interested in, because
Speaker 3: I still hold out hope and sort of some sense
Speaker 3: of self preservation, but that there is a future where
Speaker 3: the two are better. Just how to offload what job
Speaker 3: and in what sort of system that ultimately becomes. Maybe
Speaker 3: it's five agents, maybe it's ten, maybe it's a thousand.
Speaker 3: You know, we don't know the answer yet. We're just
Speaker 3: barely scratching the surface. But in three years time, I
Speaker 3: expect this to be fairly common, that clinicians of all
Speaker 3: types will be working alongside and or even consulting with
Speaker 3: some of these systems for their care their patients.
Speaker 1: And what is the adoption rate today? I mean, how
Speaker 1: far what would need to happen for this, you know
Speaker 1: paper that you've written in the system that you developed
Speaker 1: to be widely deployed in US or global healthcare.
Speaker 3: In a very practical sense, there is a lot of
Speaker 3: regulation around this, and regulation requires very rigorous study and
Speaker 3: evidence and real world deployment, all the things that you
Speaker 3: would expect right if you're you know, care team is
Speaker 3: using some of these things to take to take care
Speaker 3: of you and your health problems generating that evidence, working
Speaker 3: with policy makers, trying to figure out exactly what evidence
Speaker 3: would get to the point where we can say definitively
Speaker 3: this is at standard of care or beyond and it
Speaker 3: should be used and here's how you use it. Those
Speaker 3: are very mechanical, but they're very important. It may also
Speaker 3: require a change in how we approach the regulation of
Speaker 3: medical software because these kinds of systems are challenging our
Speaker 3: traditional software that we have used for decades in healthcare.
Speaker 4: Right, they're very different.
Speaker 3: They're non deterministic, they have moments of brilliance and moments
Speaker 3: of you know, stupidity. I should say, right, you've seen
Speaker 3: these kind of things, and so how do we actually
Speaker 3: design a system where it's safe, effective, and actually improving outcomes?
Speaker 4: And that's ultimately the evidence we have to generate.
Speaker 1: Yeah, I mean beyond stupid mistakes. How do you see
Speaker 1: the risks here? I mean we're seeing this research around
Speaker 1: you know the problems of cognitive offloading with AI, some
Speaker 1: suggestions that if you use AI too much you become
Speaker 1: dumba and deskill yourself. I mean, is there a risk
Speaker 1: of de skilling doctors? Like what are some of the
Speaker 1: maybe intangible but nonetheless medium time risks that we should
Speaker 1: be considering here.
Speaker 3: What they refer to a skill atrophy as real, and
Speaker 3: we've seen this in various other disciplines too. I think
Speaker 3: it will also require a shift in how we think
Speaker 3: and perform our knowledge work jobs. And in one way
Speaker 3: this has been sort of looked at is via the
Speaker 3: idea of meta cognition. So rather than you having to
Speaker 3: be the central source of decision making, are there things
Speaker 3: that you can manage? So the imagine you managing a
Speaker 3: team of a these agents. You have a goal, but
Speaker 3: you're offloading some of the cognitive tasks to those agents.
Speaker 3: Those are some of the early discussions around it. But
Speaker 3: I fundamentally believe that everyone that's in a knowledge work
Speaker 3: industry or role will have to rethink how that role
Speaker 3: evolves in the future. And this is kind of that
Speaker 3: first step, at least for us in the healthcare space,
Speaker 3: which is that do you need to memorize all these
Speaker 3: facts or do you just need to be able to
Speaker 3: have the right judgment and know which where the models
Speaker 3: are good and not good, and be able to fill
Speaker 3: those gaps and manage it like you would a team,
Speaker 3: like any manager would know one oh one, this person's
Speaker 3: good at this, they have some weaknesses here, right, I'm
Speaker 3: going to sign this task to them versus this.
Speaker 4: You know.
Speaker 3: That's that metacognition world that where I think we're rapidly
Speaker 3: heading towards, and healthcare is going to have to figure
Speaker 3: out a way to do that as well.
Speaker 1: Yeah, I mean the other question around deployment is conflict
Speaker 1: of interest. Right, So the previous research I've seen is
Speaker 1: all around AI versus human doctors. But this element you've
Speaker 1: added is to cost as well. It's not just outperforming,
Speaker 1: but it's outperforming at less costs in terms of tests.
Speaker 1: Is a really interesting element, but it adds the potential
Speaker 1: for major conflict of interests on both sides.
Speaker 2: Right.
Speaker 1: So for example, I'm British, grew up with the NH
Speaker 1: and one of the consistent themes of the NHS was
Speaker 1: death panels. Are there bureaucrats deciding when people should die?
Speaker 1: What is the appropriate level of care to give to
Speaker 1: people to prevent them from dying, given that it's a
Speaker 1: drain on a public budget. That's in the UK. Here
Speaker 1: in the US we have these for profit healthcare model
Speaker 1: where there is an incentive which if you're insured do
Speaker 1: you sometimes worry about that your position or healthcare system
Speaker 1: is pushing you through numerous medical procedures because ultimately it's
Speaker 1: a profit center and you may not actually need them.
Speaker 1: So how do you begin to grapple with those problems
Speaker 1: when you think about a system like this.
Speaker 3: These are problems that have existed even as you know
Speaker 3: before before AI, and I think that the responsibility for
Speaker 3: those of us kind of generating the evidence and the capabilities,
Speaker 3: and kind of displaying the rationale behind how these things
Speaker 3: work or don't work and where they work is a
Speaker 3: conversation entirely from both the economics and the cultural societal
Speaker 3: aspects of just how we deliber care. I think it
Speaker 3: wouldn't be a controversial statement to say that, at least
Speaker 3: from the US perspective, that our healthcare system is not ideal. Right,
Speaker 3: And that's true whether you're in a capitated system, a
Speaker 3: fee for service system, or a government based system like
Speaker 3: the VA. I have to hope that the better angels
Speaker 3: prevail here. But I agree with you and share your
Speaker 3: concerns that in the wrong hands, or with the various
Speaker 3: misalignments that happen in these systems at all different levels,
Speaker 3: we could end up causing some disruption in a way
Speaker 3: that we aren't hoping to see.
Speaker 1: In the end, it was an interesting blog post that
Speaker 1: you wrote around cancer care, and it al really struck
Speaker 1: me because I mean, I don't want to put the
Speaker 1: words into your mouth, but as I read it, if
Speaker 1: you are one of the lucky few who gets to
Speaker 1: go to one of the great cancer centers like M
Speaker 1: d Anderson when you're sick with cancer and have access
Speaker 1: to these cross field panel of experts who, as you
Speaker 1: mentioned earlier, sub or sub sub or even subsub sub specialized,
Speaker 1: you have a measurably better outcome, whereas in fact, most
Speaker 1: people in the US and certainly almost all people practically
Speaker 1: speaking globally don't have access to these cancer centers. Talk
Speaker 1: about that and about this idea of the multi agentic
Speaker 1: AI and how it sort of reflects or refracts what
Speaker 1: we've been talking about with the diagnosis piece.
Speaker 3: Yeah, this was a very important So, yeah, thanks for
Speaker 3: bringing this up. I think a lot of people don't
Speaker 3: know some of the inner workings of healthcare where some
Speaker 3: of the really big bottlenecks are in terms of getting
Speaker 3: the best possible outcome, and one of them is in
Speaker 3: cancer care, as you're pointing out where some of the
Speaker 3: leading centers, and in particularly larger cities, they have the
Speaker 3: ability to bring specialists from all different disciplines together to
Speaker 3: discuss the patient's care, and that's called the tumor boarder
Speaker 3: multidimary tumor board. The reason that not everyone can do
Speaker 3: that is not just because they don't maybe have that
Speaker 3: specialist in house, but also because of the massive amount
Speaker 3: of prep time it takes to gather all the information.
Speaker 3: It's not just the patient's data that you have to gather.
Speaker 3: You have to gather what clinical trials are new and
Speaker 3: availed in this patient eligible for, what does the latest
Speaker 3: literature say, And someone has to go through all that information,
Speaker 3: prepare that and then present it to a group. And
Speaker 3: what we found from the ASCO, which is the large
Speaker 3: society in cancer care in the US, was that it
Speaker 3: takes between two and a half and three and a
Speaker 3: half hours of preparation time per patient, and some centers
Speaker 3: run thousands of these tumor boards a year, and those
Speaker 3: are the ones that have the most resources and certainly
Speaker 3: the most access. The idea of AI, fundamentally for me,
Speaker 3: and the reason I'm in this field is that I
Speaker 3: want to democratize that experience for everyone, increase the access.
Speaker 3: So no matter where you live or mate matter what
Speaker 3: you do for a living, should that same level of
Speaker 3: precision when it comes to your healthcare. And so this
Speaker 3: was that first step to that. I do think this
Speaker 3: is going to continue to evolve back to our conversation
Speaker 3: around managing a team of experts as your primary physician,
Speaker 3: could I call on a team of expert agents to
Speaker 3: help walk through some of the things that we might
Speaker 3: not be considering in our fifteen minutes we have together
Speaker 3: once every six months or whatever that looks like. I'm
Speaker 3: very hopeful that given the right circumstances in the way
Speaker 3: the technology is progressing, we're going to get to a place,
Speaker 3: I think, in a perfect world at least where the
Speaker 3: access for every patient is equivalent to those who may
Speaker 3: have access to the best resources.
Speaker 1: I mean, you mentioned that twenty percent of all AI
Speaker 1: search is or op to twenty percent of a AI
Speaker 1: searches are around medicine, which is fascinating. I didn't know that.
Speaker 1: But there are of course other people who don't want
Speaker 1: AI in the healthcare settings, or who worried about their
Speaker 1: human docture or their primary care physician being replaced by
Speaker 1: an unfeeling machine. What do you say to them?
Speaker 3: It's interesting, I think going all the way back to
Speaker 3: the earliest days of search, that stat was still about
Speaker 3: the same. Up to twenty percent of Internet searches were
Speaker 3: healthcare related. And we're seeing two interesting trends. One from
Speaker 3: the economists that showed that these searches that are going
Speaker 3: on today in the typical search engines, the one that's
Speaker 3: going down the fastest is healthcare. Isn't that interesting because
Speaker 3: where are people going?
Speaker 4: Then? Well, they're probably going to the models. So I
Speaker 4: actually push back on that.
Speaker 3: I think that most people want to be educated about
Speaker 3: their medical condition, and they want to be they want
Speaker 3: to feel safe and free to ask questions about their
Speaker 3: own healthcare and essentially infinitely patient knowledgeable sort of oracle environment.
Speaker 3: And again we're not there yet, so I don't want
Speaker 3: to make that claim. But I even me, I put
Speaker 3: my data into these models and ask questions about it,
Speaker 3: and I walk away sometimes learning something or at least
Speaker 3: what I should be asking my physicians. So again, would
Speaker 3: I rather do that than any healthcare Not me personally,
Speaker 3: I do want to have that relationship with my physicians,
Speaker 3: but I also want to walk in much more knowledgeable,
Speaker 3: so I feel like we're on a pure level when
Speaker 3: we're speaking about my care decisions.
Speaker 4: Matt, thank you, Thank you so much.
Speaker 2: As that's it for this week for tech Stuff. I'm
Speaker 2: Cara Price and.
Speaker 1: I'm as Valosha And this episode was produced by Eliza Dennis,
Speaker 1: Tyler Hill and Melissa Slaughter. It was executive produced by
Speaker 1: me Cara Price and Kate Osborne for Kaleidoscope and Katria
Speaker 1: Novelle for iHeart Podcast. The Engineer is Behit Fraser and
Speaker 1: Jack Insley mixed this episode. Kyle Murdoch wrote our theme song.
Speaker 1: Please do rate, review and reach out to us at
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Speaker 1: from you.