Pełna transkrypcja odcinka Coauthor roundtable: Reflecting on healthcare economics, biomedical research, and medical education

ability  to, kind of, learn or adapt as a result. 00:05:05.560 --> 00:05:11.200 LEE: So let me start with you, Zak. Does that make  sense to you? Are you seeing something similar? 00:05:11.200 --> 00:05:17.600 KOHANE: I thought it was incredibly insightful  because we discussed on our earlier podcast how   00:05:18.240 --> 00:05:22.560 a chief AI officer in one  of the healthcare hospitals,   00:05:22.560 --> 00:05:28.080 in one of the healthcare systems,  was highly regulating the use of AI,   00:05:28.080 --> 00:05:34.400 but yet in her own practice on her smartphone  was using all these AI technologies. 00:05:34.400 --> 00:05:42.296 And so it's insightful that on the one hand,  she is increasing her personal productivity, … 00:05:42.296 --> 00:05:42.308 LEE: Right. 00:05:42.308 --> 00:05:50.720 KOHANE: … and perhaps she's increasing her  quality of her care. But it's very hard for   00:05:50.720 --> 00:05:56.800 the healthcare system to actually realize  any gains. It's unlikely … let's put it this   00:05:56.800 --> 00:06:03.480 way. It would be for her a defeat if they  said, “Now you should see more patients.” 00:06:03.480 --> 00:06:04.072 LEE: Yes. [LAUGHS] 00:06:04.072 --> 00:06:10.400 KOHANE: Now, I'm not saying that won't  happen. It could happen. But, you know,   00:06:10.400 --> 00:06:17.360 gains of productivity are really at the individual  level of the doctors. And that's why they're   00:06:17.360 --> 00:06:25.440 adopting it. That's why the ambient dictation  tools are so successful. But really turning it   00:06:25.440 --> 00:06:32.640 into things that matter in terms of productivity  for healthcare, namely making sure that patients   00:06:32.640 --> 00:06:37.440 are getting healthy, requires that every piece  of the puzzle works well together. You know,   00:06:37.440 --> 00:06:42.320 it's well-tread ground to talk about how  patients get very expensive procedures,   00:06:42.320 --> 00:06:46.936 like a cardiac transplant, and then go home,  and they’re not put on blood thinners … 00:06:46.936 --> 00:06:46.948 LEE: Right. 00:06:46.948 --> 00:06:52.560 KOHANE: … and then they get a stroke. You know,  the chain is as strong as the weakest link. And   00:06:52.560 --> 00:07:00.880 just having AI in one part of it is not going  to do it. And so hospitals, I think, are doubly   00:07:01.440 --> 00:07:10.560 burdened by the fact that, (A) they tend to not  like innovation because they are high-revenue,   00:07:10.560 --> 00:07:17.520 low-margin companies. But if they want it  implemented effectively, they have to do it   00:07:17.520 --> 00:07:25.120 across the entire processes of healthcare, which  are vast and not completely under their control. 00:07:25.120 --> 00:07:28.320 LEE: Yeah. Yep. You know, that was Sara Murray,   00:07:28.320 --> 00:07:32.160 who's the chief health AI  officer at UC San Francisco. 00:07:32.160 --> 00:07:36.320 And then, you know, Carey, remember,  we were puzzled by Chris Longhurst’s   00:07:36.320 --> 00:07:44.880 finding in a controlled study that the, you  know, having an AI respond to patient emails   00:07:44.880 --> 00:07:52.000 didn't seem to lead to any, I guess you would  call it, productivity benefits. I remember we   00:07:52.000 --> 00:07:57.360 were both kind of puzzled by that. I wonder if  that's related to what Ethan is saying here. 00:07:57.360 --> 00:08:02.160 GOLDBERG: I mean, possibly, but I  think we've seen since then that   00:08:02.160 --> 00:08:06.560 there have been multiple studies  showing that in fact using AI can   00:08:06.560 --> 00:08:11.920 be extremely effective or helpful,  even, for example, for diagnosis. 00:08:11.920 --> 00:08:16.320 And so I find just from the patient point of view,   00:08:16.320 --> 00:08:22.240 it kind of drives me crazy that you have  individual physicians using AI because   00:08:22.240 --> 00:08:26.560 they know that it will improve the care  that they're offering. And yet you don't   00:08:26.560 --> 00:08:31.520 have their institutions kind of stepping up  and saying, “OK, these are the new norms.” 00:08:31.520 --> 00:08:34.720 By the way, Ethan Mollick is a  national treasure, right. Like,   00:08:34.720 --> 00:08:39.335 he is the classic example of someone  who just stepped up at this moment … 00:08:39.335 --> 00:08:39.346 LEE: Yeah. 00:08:39.346 --> 00:08:44.080 GOLDBERG: … when we saw this extraordinary  technological advance. And he's not only stepping   00:08:44.080 --> 00:08:50.320 up for himself. He's spreading the word to the  masses that this is what these things can do. 00:08:50.320 --> 00:08:54.800 And so it's frustrating to see the  institutions not stepping up and   00:08:54.800 --> 00:08:57.360 instead the individual doctors having to do it. 00:08:57.360 --> 00:09:00.000 KOHANE: But he made another  very interesting point,   00:09:00.000 --> 00:09:07.360 which was that the reason that he could  be so informative to not only the public   00:09:07.360 --> 00:09:13.360 but practitioners of AI is these things would  emerge out of the shop, and they would not be   00:09:15.200 --> 00:09:20.240 aged too long, like a fine wine, before  they were just released to the public. 00:09:20.240 --> 00:09:28.480 And so he was getting exposure to these models  just weeks after some of the progenitors had first   00:09:28.480 --> 00:09:34.160 seen it. And therefore, because he's actually  a really creative person in terms of how he   00:09:34.160 --> 00:09:44.400 exercises models, he sees uses and problems  very early on. But the point is institutions,   00:09:44.400 --> 00:09:50.080 think about how much they are disadvantaged.  They're not Ethan Mollick. They're not the   00:09:50.080 --> 00:09:55.920 progenitors. So they're even further behind.  So it's very hard. If you talk to most of the   00:09:55.920 --> 00:10:01.720 C-suite of hospitals, they'd be delighted to  know as much about the impact as Ethan Mollick.

00:10:01.720 --> 00:10:08.560 LEE: Yeah. By the way, you know, I picked  out this quote because within Microsoft,   00:10:08.560 --> 00:10:12.400 and I suspect every other software company,  we're seeing something very similar,   00:10:12.400 --> 00:10:19.360 where individual programmers are 20 to 30% more  productive just in the number of lines of code   00:10:19.360 --> 00:10:23.840 they write per day or the number of pull  requests per week. Any way you measure it,   00:10:23.840 --> 00:10:31.680 it's very consistent. And yet by the time you get  to, say, a 25-person software engineering team,   00:10:31.680 --> 00:10:37.360 the productivity of that whole  team isn't 25% more productive. 00:10:37.360 --> 00:10:40.640 Now, that is starting to change because  we're starting to figure out that, well,   00:10:40.640 --> 00:10:48.400 maybe we should reshape how the team operates.  And there's more of an orientation towards having,   00:10:48.400 --> 00:10:53.040 you know, smaller teams of full-stack  developers. And then you start to see   00:10:53.040 --> 00:11:00.000 the gains. But if you just keep the team  organized in the usual way, there seems to   00:11:00.000 --> 00:11:04.320 be a loss. So there's something about what Ethan  was saying that resonated very strongly with me. 00:11:04.320 --> 00:11:08.240 GOLDBERG: But I would argue that it's not  just productivity we're talking about. There's   00:11:08.240 --> 00:11:14.160 a moral imperative to improve the care.  And if you have tools that will do that,   00:11:14.160 --> 00:11:17.040 you should be using them or trying harder to. 00:11:17.040 --> 00:11:18.160 LEE: Right. Yep. 00:11:18.160 --> 00:11:25.520 KOHANE: I think, yes, first of all,  absolutely you would. Unfortunately,   00:11:25.520 --> 00:11:29.920 most of the short-term productivity  measures will not measure improvements   00:11:29.920 --> 00:11:35.520 in the quality of care because it takes  a long time to die even with bad care. 00:11:35.520 --> 00:11:40.400 And so that doesn't show up right away.  But I think what Peter just said actually   00:11:40.400 --> 00:11:49.120 came across in several of the podcasts,  which is that it's very tricky trying to   00:11:49.120 --> 00:11:53.040 shoehorn these things into making what  we're already doing more productive. 00:11:53.040 --> 00:11:54.720 GOLDBERG: Yeah. Existing structures. 00:11:54.720 --> 00:11:59.120 KOHANE: Yeah. And I know, Carey,  that you've raised this issue   00:11:59.680 --> 00:12:08.160 many times. But it really calls into question,  what should we be doing with our time with   00:12:08.160 --> 00:12:16.640 doctors? And they are a scarce resource. And  what is the most efficient way to use them? 00:12:16.640 --> 00:12:18.640 You know, I remember we [The New England Journal  of Medicine AI] published a paper of someone who   00:12:18.640 --> 00:12:25.680 was able to use AI to increase the throughput  of their emergency room by actually more   00:12:25.680 --> 00:12:34.320 appropriately having the truly sick people in the  sick queue, in the triage queue, for urgent care. 00:12:34.320 --> 00:12:38.240 And so I think we're going to have  to think that way more broadly,   00:12:38.240 --> 00:12:43.680 about we don't have to now look at  every patient as an unknown with   00:12:44.240 --> 00:12:48.880 maybe a few pointers on diagnosis. We  can have a fairly extensive profiling. 00:12:48.880 --> 00:12:53.520 And I know that colleagues in Clalit  [Health Services] in Israel, for example,   00:12:53.520 --> 00:12:58.720 are using the overall trajectory of the  patient and some considerations about   00:12:58.720 --> 00:13:01.840 utilities to actually figure  out who to see next week. 00:13:01.840 --> 00:13:08.000 LEE: Yeah, you know, what you said brings up  another maybe connection to one thing that we   00:13:08.000 --> 00:13:15.040 see also in software development. And it relates  to also what we were discussing earlier: about the   00:13:15.040 --> 00:13:21.120 last thing a doctor wants is to have a tool that  allows them to see even yet more patients per day. 00:13:21.680 --> 00:13:25.760 So in software development,  there's always this tension. Like,   00:13:25.760 --> 00:13:29.200 how many lines of code can you write per  day? That's one productivity measure. 00:13:29.920 --> 00:13:35.840 But sometimes we're taught, well,  don't write more lines of code per day,   00:13:35.840 --> 00:13:42.240 but make sure that your code is well structured.  Take the time to document it. Make sure it's fully   00:13:42.240 --> 00:13:46.080 commented. Take the time to talk to your  fellow software engineering team members   00:13:46.080 --> 00:13:50.560 to make sure that it's well coordinated.  And in the long run, even if you're writing   00:13:50.560 --> 00:13:55.920 half the number of lines of code per day, the  software process will be far more efficient. 00:13:55.920 --> 00:14:00.160 And so I've wondered whether there's a  similar thing where doctors could see   00:14:00.160 --> 00:14:06.560 20% fewer patients in a day, but if they take  the time and also had AI help to coordinate,   00:14:06.560 --> 00:14:12.240 maybe a patient's journey might be half  as long. And therefore, the health system   00:14:12.240 --> 00:14:16.840 would be able to see twice as many patients  in a year's period or something like that. 00:14:16.840 --> 00:14:21.840 KOHANE: So I think you've  “nerd sniped” me because you   00:14:21.840 --> 00:14:28.240 [LAUGHTER]—which is all too easy—but I  think there's a central issue here. And   00:14:28.240 --> 00:14:32.640 I think this is the stumbling block between  what Ethan's telling us about between the   00:14:32.640 --> 00:14:37.600 individual productivity and the larger  productivity, is the team's productivity. 00:14:37.600 --> 00:14:43.600 And there is actually a good analogy  in computer science and that's,   00:14:43.600 -->

00:14:45.600 uh, Brooks’s “mythical man-month,” ... 00:14:45.600 --> 00:14:46.720 LEE: Yes, exactly. 00:14:46.720 --> 00:14:50.400 KOHANE: … where he shows how you  can have more and more resources,   00:14:50.400 --> 00:14:56.400 but when the coordination starts failing, because  you have so many, uh, individuals on the team,   00:14:56.400 --> 00:15:03.360 you start falling apart. And so even if the,  uh, individual doctors get that much better,   00:15:03.360 --> 00:15:06.400 yeah, they take better care of  patients, make less stupid things. 00:15:06.400 --> 00:15:11.680 But in terms of giving the “I get you into  the emergency room, and I get you out of   00:15:11.680 --> 00:15:15.600 a hospital as fast as possible, as safely  as possible, as effectively as possible,”   00:15:15.600 --> 00:15:21.920 that's teamwork. And we don't do it. And we're  not really optimizing our tools for that. 00:15:21.920 --> 00:15:26.560 GOLDBERG: And just to throw in a little reality  check, I'm not aware of any indication yet that   00:15:26.560 --> 00:15:32.635 AI is in any way shortening medical journeys  or making physicians more efficient. Yet … 00:15:32.635 --> 00:15:32.946 LEE: Right. 00:15:32.946 --> 00:15:33.840 GOLDBERG: … at least. Yeah. 00:15:33.840 --> 00:15:38.400 LEE: Yes. So I think, you know, with respect  to our book, critiquing our book, you know,   00:15:38.400 --> 00:15:42.880 I think it's fair to say we were fairly  focused or maybe even fixated on the   00:15:42.880 --> 00:15:51.360 individual doctor or nurse or patient, and we  didn't really, at least I never had a time where   00:15:51.360 --> 00:15:57.480 I stepped back to think about the whole care  coordination team or the whole health system. 00:15:57.480 --> 00:16:02.800 KOHANE: And I think that's right. It's  because, first of all, you weren’t thinking   00:16:02.800 --> 00:16:06.560 about it? It's not what we're taught in  medical school. We're not taught to talk   00:16:06.560 --> 00:16:11.840 about team communication excellence.  And I think it's absolutely essential. 00:16:12.800 --> 00:16:17.200 There’s a … what’s the … there was  an early … [Terry] Winograd. And   00:16:17.200 --> 00:16:22.720 he was trying to capture what are the  different kinds of actions related to   00:16:22.720 --> 00:16:27.040 pronouncements that you could expect and how could  AI use that. And that was beginning to get at it. 00:16:27.040 --> 00:16:33.200 But I actually think this is dark matter of  human organizational technology that is not   00:16:33.200 --> 00:16:38.480 well understood. And our products don't do  well. You know, we can talk about all the   00:16:38.480 --> 00:16:44.859 groupware things that are out there. But  they all don't quite get to that thing. 00:16:44.859 --> 00:16:44.872 LEE: Right. 00:16:44.872 --> 00:16:48.960 KOHANE: And I can imagine an  AI serving as a team leader,   00:16:48.960 --> 00:16:54.400 a really active team leader, a real  quarterback of, let's say, a care team. 00:16:54.400 --> 00:16:58.800 LEE: Well, in fact, you know, we have been  trying to experiment with this. My colleague,   00:16:58.800 --> 00:17:03.920 Matt Lungren, who was also one of the interviewees  early on, has been working with Stanford   00:17:04.960 --> 00:17:12.480 Medicine on a tumor board AI agent—something  that would facilitate tumor board meetings. 00:17:12.480 --> 00:17:17.760 And the early experiences are pretty  interesting. Whether it relates to   00:17:17.760 --> 00:17:22.480 efficiency or productivity I think remains to  be seen, but it does seem pretty interesting. 00:17:22.480 --> 00:17:23.440 But let's move on. 00:17:23.440 --> 00:17:24.706 GOLDBERG: Well, actually, Peter, … LEE: Oh, go ahead. 00:17:24.706 --> 00:17:26.942 GOLDBERG: … if you're willing  to not quite move on yet … 00:17:26.942 --> 00:17:27.600 LEE: [LAUGHS] All right. 00:17:27.600 --> 00:17:33.200 GOLDBERG: … this kind of segues into one of,  I think, the most provocative questions that   00:17:33.200 --> 00:17:40.160 arose in the course of these episodes and  that I'd love to have you answer, which was,   00:17:40.160 --> 00:17:46.160 remember, it was a question at a gathering  that you were at, and you were asked, “Well,   00:17:46.160 --> 00:17:53.040 you're focusing a lot on potential AI effects  on individual patient and physician experiences.   00:17:53.040 --> 00:17:59.360 But what about the revolution, right? What  about, like, can you be more big-picture and   00:17:59.360 --> 00:18:07.360 envision how generative AI could actually, kind  of, overturn or fix the broken system, right?” 00:18:07.360 --> 00:18:10.840 I'm sure you've thought about that  a lot. Like, what's your answer? 00:18:10.840 --> 00:18:17.360 LEE: You know, I think ultimately, it will  have to. For it to really make a difference,   00:18:17.360 --> 00:18:23.600 I think that the normal processes, our normal  concept of how healthcare is delivered—how   00:18:23.600 --> 00:18:27.200 new medical discoveries are made and brought   00:18:27.200 --> 00:18:32.000 into practice—I think those things  are going to have to change a lot. 00:18:32.800 --> 00:18:38.160 You know, one of the things I think about a  lot right at the moment is, you know, we tend   00:18:38.160 --> 00:18:45.920 to think about, let's say, medical diagnosis  as a problem-solving exercise. And I think,   00:18:46.720 --> 00:18:51.520 at least at the Kaiser Permanente  School of Medicine, the instruction   00:18:51.520 --> 00:18:58.000 really treats it as a kind of detective  thing based on a lot of knowledge about   00:18:58.000 --> 00:19:01.680 biology and biomedicine and  human condition, and so on. 00:19:01.680 --> 00:19:08.480 But there's another way to think about it,  given AI, which is when you see a patient and   00:19:08.480 --> 00:19:14.160 you develop some data, maybe through a physical  exam, labs, and so on, you can just simply ask,  

00:19:14.160 --> 00:19:21.040 “You know, what did the 500 other people who are  most similar to this experience, how were they   00:19:21.040 --> 00:19:25.040 diagnosed? How were they treated? What were  their outcomes? What were their experiences?” 00:19:25.040 --> 00:19:28.960 And that's really a fundamentally  different paradigm. And it just   00:19:28.960 --> 00:19:30.880 seems like at least the technical means   00:19:31.520 --> 00:19:36.720 will be there. And by the way, that also then  relates to [the questions]: “And what was most   00:19:36.720 --> 00:19:43.600 efficacious cost-wise? What was most efficient in  terms of the total length of the patient journey?   00:19:43.600 --> 00:19:48.800 How does this relate to my quality scores so I  can get more money from Medicare and Medicaid?” 00:19:48.800 --> 00:19:54.400 All of those things, I think, you  know, we're starting to confront. 00:19:54.400 --> 00:19:56.960 One of the other episodes that  we're going to talk about,   00:19:56.960 --> 00:20:03.760 was my interview with two medical students.  Actually, thinking of a Morgan Cheatham as   00:20:03.760 --> 00:20:09.440 just a medical student or medical resident  [LAUGHTER] is a little strange. But he is. 00:20:10.160 --> 00:20:17.760 One of the things he talks about is the  importance that he placed in his medical   00:20:17.760 --> 00:20:25.520 training about adopting AI. So, Zak,  I assume you see this also with some   00:20:25.520 --> 00:20:29.440 students at Harvard Medical School. And  the other medical student we interviewed,   00:20:29.440 --> 00:20:33.760 Daniel Chen, seemed to indicate this, too,  where it seems like it's the students who are   00:20:33.760 --> 00:20:38.880 bringing AI into the medical education ahead  of the faculty. Does that resonate with you? 00:20:38.880 --> 00:20:46.080 KOHANE: It absolutely resonates with me.  There are students I run into who, honestly,   00:20:46.080 --> 00:20:50.960 my first thought when I'm talking to them is, why  am I teaching you [LAUGHTER], and why are you not   00:20:50.960 --> 00:20:56.000 starting a big AI company, AI medicine company,  now and really change healthcare instead of going   00:20:56.000 --> 00:21:04.160 through the rest of the rigmarole? And I think  broadly, higher education has a problem there,   00:21:04.160 --> 00:21:11.760 which is we have not embraced, again, going  back to Ethan, a lot of the tools that   00:21:12.800 --> 00:21:17.280 can be used. And it's because we don't  know necessarily the right way to teach   00:21:17.280 --> 00:21:23.280 them. And so far, the only lasting heuristic  seems to be: use them and use them often. 00:21:23.280 --> 00:21:29.920 And so it's an awkward thing, where the  person who knows how to use the AI tools   00:21:29.920 --> 00:21:34.800 now in the first-year medical school can  teach themselves better and faster than   00:21:34.800 --> 00:21:39.720 anybody else in their class who is just  relying

on the medical school curriculum. 00:21:39.720 --> 00:21:44.560 LEE: Now, the reason I brought up Morgan now  after our discussion with Ethan Mollick is   00:21:46.160 --> 00:21:49.820 Morgan also talked about AI  collapsing medical specialties. 00:21:49.820 --> 00:21:53.360 GOLDBERG: Yes. LEE: And so let's hear this snippet from him. 00:21:53.360 --> 00:21:58.160 MORGAN CHEATHAM: AI collapses medical specialties  onto themselves, right. You have the canonical   00:21:58.160 --> 00:22:03.200 example of the cardiologist, you know, arguing  that we should diuresis and maybe the nephrologist   00:22:03.200 --> 00:22:09.360 arguing that we should, you know, protect the  kidneys. And how do two disciplines disagree   00:22:09.360 --> 00:22:14.160 on what is right for the patient when in  theory, there is an objective best answer   00:22:14.160 --> 00:22:19.680 given that patient’s clinical status? … So I’m  interested in this question of whether medical   00:22:19.680 --> 00:22:24.720 specialties themselves need to evolve. And if we  look back in the history of medical technology,   00:22:24.720 --> 00:22:29.760 there are many times where a new technology  forced a medical specialty to evolve. 00:22:29.760 --> 00:22:33.760 LEE: So on the specific  question about specialties,   00:22:33.760 --> 00:22:38.000 Zak, do you have a point of view? And  let me admit, first of all, for us,   00:22:38.000 --> 00:22:42.880 all three of us, we didn't have any clue  about this in our book. I don't think. 00:22:42.880 --> 00:22:44.960 KOHANE: Not much. Not much of a clue. 00:22:46.400 --> 00:22:52.160 So I'm reminded of a New Yorker cartoon where  you see a bunch of surgeons around the patient,   00:22:52.160 --> 00:22:55.360 and someone says, “Is that a spleen?” And  it says, “I don't know. I slept during   00:22:55.360 --> 00:23:01.040 the spleen lecture,” [LAUGHTER] and …  or “I didn't take the spleen course.” 00:23:01.040 --> 00:23:05.680 And yet when we measure things, we  measure things much more than we   00:23:05.680 --> 00:23:09.040 think we are doing. So for example, we  [NEJM AI] just published a paper where   00:23:09.680 --> 00:23:13.520 echocardiograms were being done. And it  turns out those ultrasound waves just   00:23:13.520 --> 00:23:19.360 happen to also permeate the liver. And you can  actually diagnose on the way with AI all the   00:23:19.360 --> 00:23:24.960 liver disease that is in—and treatable  liver disease—that's in those patients. 00:23:24.960 --> 00:23:31.920 But if you're a cardiologist, “Liver? You know,  I slept through liver lecture.” [LAUGHTER] And   00:23:31.920 --> 00:23:44.800 so I do think that, (A) the natural, often  guild/dollar-driven silos in medicine are   00:23:44.800 --> 00:23:52.800 less obvious to AI, despite the fact that they  do exist in departments and often in chapters. 00:23:52.800 --> 00:23:56.720 But Morgan's absolutely right. I  can tell you as an endocrinologist,   00:23:57.520 --> 00:23:59.760 if I have a child in the ICU, the endocrinologist,   00:23:59.760 --> 00:24:04.160 the nephrologist, and the neurosurgeon  will argue about the right thing to do. 00:24:04.160 --> 00:24:13.120 And so in my mind, the truly revolutionary thing  to do is to go back to 1994 with Pete Szolovits,   00:24:13.680 --> 00:24:19.760 the Guardian Angel Project. What I think  you need is a process. And the process is   00:24:19.760 --> 00:24:25.200 the quarterback. And the quarterback has  only one job: take care of the patient. 00:24:25.200 --> 00:24:30.400 And it should be thinking all the time about  the patient. What's the right thing? And can   00:24:30.400 --> 00:24:34.480 be as school-marmish or not about,  “Zak, you're eating this or that or   00:24:34.480 --> 00:24:42.720 exercise or sleep,” but also, “Hey, surgeons and  endocrinologists, you're talking about my host,   00:24:42.720 --> 00:24:48.080 Zak. This is the right way because this  problem and this problem and our best   00:24:48.080 --> 00:24:52.240 evidence is this is the right way to get rid  of the fluid. The other ways will kill him.” 00:24:52.240 --> 00:24:55.440 And I think you need an authoritative quarterback   00:24:55.440 --> 00:24:59.040 that has the view of the others  but then makes the calls. 00:24:59.040 --> 00:25:02.880 LEE: Is that quarterback going to be AI or human? 00:25:02.880 --> 00:25:09.120 KOHANE: Well, for the very lucky people, it'll  be a human augmented by AI, super concierge. 00:25:09.120 --> 00:25:14.800 But I think we're running out of doctors. And so  realistically, it's going to be an AI that will   00:25:14.800 --> 00:25:20.480 have to be certified in very different ways, along  the ways Dave Blumenthal says, essentially, trial   00:25:20.480 --> 00:25:25.920 by fire. Like putting residents into clinics,  we're going to be putting AIs into clinics. 00:25:26.720 --> 00:25:32.400 But what's worse, by the way, than the three  doctors arguing about care in front of the patient   00:25:32.400 --> 00:25:37.840 is, what happens so frequently, is then you see  them outpatient, and each one of them gives you   00:25:37.840 --> 00:25:46.320 a different set of decisions to make. Sometimes  that actually interact pathologically, unhealthily   00:25:46.320 --> 00:25:52.000 with each other. And only the very smart nurses  or primary care physicians will actually notice   00:25:52.000 --> 00:25:55.680 that and call, quote, a “family meeting,” or  bring everybody in the same room to align them. 00:25:55.680 --> 00:26:03.200 LEE: Yeah, I think this idea of quarterback  is really very, very topical right now because   00:26:03.200 --> 00:26:07.520 there's so much intensity in the AI  space around agents. And in fact,   00:26:07.520 --> 00:26:16.000 you know, the Microsoft AI team under Mustafa  Suleyman and Dominic King, Harsha Nori,   00:26:16.000 --> 00:26:22.560 and team just recently posted a paper on something  called

sequential diagnosis, which is basically an   00:26:22.560 --> 00:26:29.120 AI quarterback that is supposed to smartly consult  with other AI specialties. And interestingly, one   00:26:29.120 --> 00:26:35.323 of the AI agents is sort of the devil's advocate  that's always criticizing and questioning things. 00:26:35.323 --> 00:26:38.320 GOLDBERG: That’s interesting. LEE: And at least on very, very hard, rare cases, 00:26:38.320 --> 00:26:44.800 it can develop some impressive results. There's  something to this that I think is emerging. 00:26:44.800 --> 00:26:50.080 GOLDBERG: And, Peter, Morgan said something  that blew me away even more, which was,   00:26:50.080 --> 00:26:55.360 well, why do we even need specialists if  the reason for a specialist is because   00:26:55.360 --> 00:27:00.320 there's so much medical knowledge that  no single physician can know all of it,   00:27:00.320 --> 00:27:06.240 and therefore we create specialists, but  that limitation does not exist for AI. 00:27:06.240 --> 00:27:06.290 LEE: Yeah. Yeah. 00:27:06.290 --> 00:27:11.920 GOLDBERG: And so there he was kind of undermining  this whole elaborate structure that has grown   00:27:11.920 --> 00:27:16.240 up because of human limitations that  may not ultimately need to be there. 00:27:16.240 --> 00:27:20.880 LEE: Right. So now that gives me a good  segue to get back to our economist and   00:27:20.880 --> 00:27:28.640 get to something that Azeem Azhar said.  And so there's a clip here from Azeem. 00:27:28.640 --> 00:27:35.200 AZEEM AZHAR: We didn’t talk about, you know, AI  in its ability to potentially do this, which is   00:27:35.200 --> 00:27:44.960 to extend the clinician’s presence throughout the  week. You know, the idea that maybe some part of   00:27:44.960 --> 00:27:49.440 what the clinician would do if you could talk to  them on Wednesday, Thursday, and Friday could be   00:27:49.440 --> 00:27:56.800 delivered through an app or a chatbot just as a  way of encouraging the compliance, which is often,   00:27:56.800 --> 00:28:02.080 especially with older patients, one reason why  conditions, you know, linger on for longer. 00:28:02.080 --> 00:28:09.200 LEE: And, you know, in the same conversation, he  also talked about his own management of asthma and   00:28:09.200 --> 00:28:17.200 the fact that he's been managing this for several  decades and knows more than any other human being,   00:28:17.200 --> 00:28:22.400 no matter how well medically trained, could  possibly know. And it's also very highly   00:28:22.400 --> 00:28:29.120 personalized. And it's not a big leap to imagine  AI having that sort of lifelong understanding. 00:28:29.120 --> 00:28:36.480 KOHANE: So in fact, I want to give credit back  to our book since you insulted us. [LAUGHTER] You   00:28:36.480 --> 00:28:46.560 challenged us. You doubted us. We do have at the  end of the book a AI which is helping this woman   00:28:46.560 --> 00:28:53.022 manage her way through life. It's quarterbacking  for the woman all these different services. 00:28:53.022 --> 00:28:53.880 LEE: Yes. KOHANE: So there. 00:28:53.880 --> 00:28:58.480 LEE: Ah, you're right. Yes. In  fact, it's very much, I think,   00:28:58.480 --> 00:29:02.760 along the lines of the vision that  Azeem laid out in our conversation. 00:29:02.760 --> 00:29:06.560 GOLDBERG: Yeah. It also reminded me of  the piece Zak wrote about his mother at   00:29:06.560 --> 00:29:11.120 one point when she was managing congestive  heart failure and she needed to watch her   00:29:11.120 --> 00:29:16.640 weight very carefully to see her fluid status.  And absolutely, there's no … I see no reason   00:29:16.640 --> 00:29:22.160 whatsoever why that couldn't be done with AI  right now. Actually, although back then, Zak,   00:29:22.160 --> 00:29:27.600 you were writing that it takes much more than  an AI [LAUGHS] to manage such a thing, right? 00:29:27.600 --> 00:29:32.880 KOHANE: You need an AI that you can  trust. Now, my mother was born in 1927,   00:29:32.880 --> 00:29:36.160 and she'd learned through the school of hard  knocks that you can't trust too many people,   00:29:36.160 --> 00:29:40.040 maybe even not your son, MD, PhD [LAUGHTER]. 00:29:40.040 --> 00:29:43.920 But what I've been surprised [by] is how,  for example, how many people are willing   00:29:43.920 --> 00:29:49.640 to trust and actually see effective use of  AI as mental health counselors, for example. 00:29:49.640 --> 00:29:50.160 GOLDBERG: Yeah 00:29:50.160 --> 00:29:54.080 KOHANE: So it may in fact be that  there's a generational thing going on,   00:29:54.080 --> 00:29:58.160 and at least there'll be some very  large subset of patients which will   00:29:58.160 --> 00:30:02.000 be completely comfortable in ways that  my mother would have never tolerated. 00:30:02.000 --> 00:30:05.920 LEE: Yeah. Now, I think we're starting  to veer into some of the core AI. 00:30:05.920 --> 00:30:11.200 And so I think maybe one of the  most fun conversations I had   00:30:11.200 --> 00:30:18.640 was in the episode with both Sébastien Bubeck,  my former colleague at Microsoft Research,   00:30:18.640 --> 00:30:25.120 and now he's at OpenAI, and Bill Gates.  And there was so much that was, I thought,   00:30:25.120 --> 00:30:30.720 interesting there. And there was one point,  I think that sort of touches tangentially   00:30:30.720 --> 00:30:37.581 on what we were just conversing about, that  Sébastien said. So let's hear this snippet. 00:30:37.581 --> 00:30:41.360 SÉBASTIEN BUBECK: And one example that I  really like, a study that recently appeared   00:30:41.360 --> 00:30:47.520 where … they were comparing doctors without and  with ChatGPT. … So this was a set of cases where   00:30:47.520 --> 00:30:55.600 the accuracy of the doctors alone was around  75%. ChatGPT alone was 90%. … But then the   00:30:55.600 --> 00:31:02.800 kicker is that doctors with ChatGPT was 80%.  Intelligence alone

is not enough. It’s also   00:31:02.800 --> 00:31:08.000 how it’s presented, how you interact with it.  And ChatGPT, it’s an amazing tool. Obviously,   00:31:08.000 --> 00:31:12.800 I absolutely love it. But it’s not … you  don’t want a doctor to have to type in,   00:31:12.800 --> 00:31:16.800 you know, prompts and use it that  way. It should be, as Bill was saying,   00:31:16.800 --> 00:31:21.160 kind of running continuously in the  background, sending you notifications. 00:31:21.160 --> 00:31:28.480 LEE: So I thought Sébastien was saying something  really profound, but I haven't been able to quite   00:31:28.480 --> 00:31:34.080 decide or settle in my mind what it is.  What do you make of what Seb just said? 00:31:34.080 --> 00:31:41.120 KOHANE: I think it's context. I think that  it requires an enormous amount of energy,   00:31:41.120 --> 00:31:46.080 brain energy, to actually correctly  provide the context that you want   00:31:46.080 --> 00:31:51.920 this thing to work on. And it's only going  to really feel like we're in a different   00:31:51.920 --> 00:31:57.600 playing field when it's listening all  the time, and it just steps right in. 00:31:57.600 --> 00:32:05.120 There is an advantage that, for example, a good  programmer can have in prompting Cursor or any   00:32:05.120 --> 00:32:12.400 of these tools to do so. But it takes effort.  And I think being in the conversation all the   00:32:12.400 --> 00:32:18.880 time so that you understand the context in the  widest possible way is incredibly important. And   00:32:18.880 --> 00:32:26.320 I think that's what Seb is getting at, which  is if we spoon feed these machines, yes, 90%. 00:32:27.120 --> 00:32:31.760 But then, talking to a human being who then  has to interact and gets distracted from   00:32:31.760 --> 00:32:38.400 whatever flow they're in and maybe even makes  them feel like an early bicycle rider who all   00:32:38.400 --> 00:32:43.200 of a sudden realizes, “I'm balancing on two  wheels—oh no!” And they fall over. You know,   00:32:43.200 --> 00:32:46.960 there's that interaction which  is negatively synergistic. 00:32:46.960 --> 00:32:54.800 And so I do think it's a very hard human-computer  engineering problem. How do we make these two   00:32:54.800 --> 00:33:00.720 agents, human and computational, work in  an ongoing way in the flow? I don't think   00:33:00.720 --> 00:33:05.600 I'm seeing anything that's particularly new. And  the things that you're beginning to hint about,   00:33:05.600 --> 00:33:12.475 Peter, in terms of agentic coordination,  I think we'll get to some of that. 33:25 00:33:12.475 --> 00:33:18.960 LEE: Yeah. Carey, does this give you any  pause? The kind of results that … they're   00:33:18.960 --> 00:33:24.480 puzzling results. I mean, the idea of  doctors with AI seeming at least in   00:33:24.480 --> 00:33:30.720 this one test—it's just one test—but it's  odd that it does worse than the AI alone. 00:33:30.720 --> 00:33:36.560 GOLDBERG: Yes. I would want to understand more  about the actual conditions of that study. 00:33:37.200 --> 00:33:43.440 From what Bill Gates said, I was most  struck by the question of resource-poor   00:33:43.440 --> 00:33:48.080 environments. That even though this was  absolutely one of the most promising,   00:33:48.080 --> 00:33:54.160 brightest perspectives that we highlighted in  the book, we still don't seem to be seeing a   00:33:54.160 --> 00:34:00.560 lot of use among the one half of humanity  that lacks decent access to healthcare. 00:34:00.560 --> 00:34:04.720 I mean, there are access problems everywhere,  including here in the United States. And it   00:34:04.720 --> 00:34:10.720 is one of the most potentially promising uses of  AI. And I thought if anyone would know about it,   00:34:10.720 --> 00:34:14.280 he would with the work that  the Gates Foundation does. 00:34:14.280 --> 00:34:20.240 LEE: You know, I think both you and Bill,  I felt, are really simpatico. You know,   00:34:20.240 --> 00:34:25.760 Bill expressed genuine surprise that more  isn't happening yet. And it really echoed,   00:34:25.760 --> 00:34:33.600 in fact, maybe even using some of the exact same  words that you've used. And so two years on,   00:34:33.600 --> 00:34:38.560 you've expressed repeatedly expecting  to have seen more out in the field by   00:34:38.560 --> 00:34:44.560 now. And then I thought Bill was saying  something in our conversation very similar. 00:34:44.560 --> 00:34:45.280 GOLDBERG: Yeah. 00:34:45.280 --> 00:34:51.120 LEE: You know, for me, I see it both ways.  I see the world of medicine really moving   00:34:51.120 --> 00:34:56.640 fast in confronting the reality of AI in  such a serious way. But at the same time,   00:34:56.640 --> 00:35:02.480 it's also hard to escape the feeling that  somehow, we should be seeing even more. 00:35:02.480 --> 00:35:07.920 So it's an odd thing, a little bit paradoxical. 00:35:07.920 --> 00:35:11.760 GOLDBERG: Yeah. I think one thing that we  didn't focus on hardly at all in the book   00:35:11.760 --> 00:35:17.120 but that we are seeing is these companies  rising up, stepping up to the challenge,   00:35:17.120 --> 00:35:22.640 Abridge and OpenEvidence, and what  Morgan describes as a new stack, right. 00:35:23.360 --> 00:35:26.240 So there is that on the flip side. 00:35:26.240 --> 00:35:32.160 LEE: Now, I want to get back to this  thing that Seb was saying. And, you know,   00:35:32.160 --> 00:35:39.120 I had to bring up the issue of sycophancy, which  we discussed at our last roundtable also. But it   00:35:39.120 --> 00:35:44.400 was particularly … at the time that Seb, Bill,  and I had our conversation, OpenAI had just gone   00:35:44.400 --> 00:35:52.720 through having to retract a fresh update of  GPT-4o because it had become too sycophantic. 00:35:53.680 --> 00:36:02.160 So I can't escape the feeling that some of  these human-computer interaction issues are  

00:36:02.160 --> 00:36:08.720 related to this tension between you want AI to  follow your directions and be faithful to you,   00:36:08.720 --> 00:36:16.640 but at the same time not agree with  you so often that it becomes a fault. 00:36:16.640 --> 00:36:23.360 KOHANE: I think it’s asking the AI to  enter into a fundamental human conundrum,   00:36:23.360 --> 00:36:28.240 which is there are extreme versions of  doublethink, and there's everyday things,   00:36:28.240 --> 00:36:33.200 everyday asks of doublethink, which  is how to be an effective citizen. 00:36:33.200 --> 00:36:39.440 And even if you're thinking, “Hmm. I'm thinking  this. I'm just not going to say it because that   00:36:39.440 --> 00:36:44.160 would be rude or counterproductive.”  Or some of the official doublethinks,   00:36:44.160 --> 00:36:50.640 where you're actually told you must say this,  even if you think something else. And I think   00:36:50.640 --> 00:36:59.120 we're giving a very tough mission for these  things: be nice to the user and be useful. 00:36:59.120 --> 00:37:03.760 And, in education, where the  thing is not always one in   00:37:03.760 --> 00:37:09.040 the same. Sometimes you have to give a  little tough love to educate someone,   00:37:09.040 --> 00:37:15.680 and doing that well is both an art  and it's also very difficult. And so,   00:37:15.680 --> 00:37:21.360 you know, I'm willing to believe that the  latest frontier models that have made the   00:37:21.360 --> 00:37:27.755 news in the last month are very high-performing,  but they're also all highlighting that tension … 00:37:27.755 --> 00:37:28.788 LEE: Yes. 00:37:28.788 --> 00:37:39.920 KOHANE: … that tension between behaving  like a good citizen and being helpful.   00:37:39.920 --> 00:37:45.440 And this gets back to what are the fundamental  values that we hope these things are following. 00:37:45.440 --> 00:37:50.240 It's not, you know, “Are these things going  to develop us into the paperclip factory?”   00:37:50.240 --> 00:37:56.480 It's more of, “Which of our values are going to  be elevated, and which one will be suppressed?” 00:37:56.480 --> 00:38:01.680 LEE: Well, since I criticized our book before, let  me pat ourselves on the back this time because,   00:38:01.680 --> 00:38:07.520 I think, pervasive throughout our book,  we were touching on some of these issues. 00:38:07.520 --> 00:38:13.440 In fact, we started the book, you know,  with GPT-4 scolding me for wanting it   00:38:13.440 --> 00:38:20.960 to impersonate Zak. And there was the  whole example of asking it to rewrite   00:38:20.960 --> 00:38:27.360 a poem in a certain way, and it kind of  silently just tried to slide, you know,   00:38:27.360 --> 00:38:33.200 without me knowing, slide by without  following through on the whole thing. 00:38:33.200 --> 00:38:38.960 And so that early version of GPT-4 was  definitely not sycophantic at all. In fact,   00:38:38.960 --> 00:38:43.672 it was just as prone to call you an idiot  if it thought you were wrong. [LAUGHTER] 00:38:43.672 --> 00:38:47.984 KOHANE: I had some very testy conversations  around my endocrine diagnosis with it. [LAUGHTER] 00:38:47.984 --> 00:38:51.440 GOLDBERG: Yeah. Well then, Peter, I would ask  you, I mean last time I asked you about, well,   00:38:51.440 --> 00:38:54.560 hallucinations, aren't those solvable?  And this time I would ask you, well,   00:38:54.560 --> 00:39:01.360 sycophancy, isn't that kind of like a dial  you can turn? Like, is that not solvable? 00:39:01.360 --> 00:39:08.560 LEE: You know, I think there are several  interlocking problems. But if we assume   00:39:08.560 --> 00:39:12.320 superintelligence, even with superintelligence,   00:39:12.320 --> 00:39:18.960 medicine is such an inexact science that  there will always be situations that are   00:39:18.960 --> 00:39:25.840 guesses that take into account other factors  of a person's life, other value judgments,   00:39:25.840 --> 00:39:30.800 exactly as Zak had pointed out in  our previous roundtable conversation. 00:39:31.520 --> 00:39:36.960 And so I think there's always going to  be an opening for either differences of   00:39:36.960 --> 00:39:42.960 opinion or agreeing with you too much.  And there are dangers in both cases.   00:39:42.960 --> 00:39:49.760 And I think they'll always be present. I  don't know that, at least in something as   00:39:49.760 --> 00:39:56.240 inexact as medical science, I don't know  that it'll ever be completely eliminated. 00:39:56.240 --> 00:39:59.520 KOHANE: And it's interesting because I was  trying to think what's the right balance,   00:39:59.520 --> 00:40:04.800 but there are patients who want to  be told this is what you do. Whereas   00:40:04.800 --> 00:40:09.840 there's other patients who want to go  through every detail of the reasoning. 00:40:09.840 --> 00:40:13.520 And it's not a matter of education.  It's really a temperamental,   00:40:13.520 --> 00:40:21.995 personality issue. And so we're going to  have to, I think, develop personalities … 00:40:21.995 --> 00:40:22.240 LEE: Yeah. 00:40:22.240 --> 00:40:30.000 KOHANE: … that are most effective for those  different kinds of individuals. And so   00:40:30.000 --> 00:40:34.160 I think that is going to be the real  frontier. Having human values and   00:40:34.160 --> 00:40:39.480 behaving in ways that are recognizable and  yet effective for certain groups of patients. 00:40:39.480 --> 00:40:40.400 LEE: Yeah. 00:40:40.400 --> 00:40:44.080 KOHANE: And lots of deep questions,  including how paternalistic do we want to be? 00:40:44.080 --> 00:40:47.920 LEE: All right, so we're getting into medical  science and hallucination. So that gives me   00:40:47.920 --> 00:40:54.720 a great segue to the conversations in  the episode on biomedical research. And   00:40:55.280 --> 00:41:00.720 one of the people that I interviewed was  Noubar Afeyan from Moderna and Flagship   00:41:00.720 -->

00:41:03.745 Pioneering. So let's listen to this snippet. 00:41:03.745 --> 00:41:09.440 NOUBAR AFEYAN: We, some hundred or so times a  year, ask “what if” questions that lead us to   00:41:09.440 --> 00:41:14.640 totally weird places of thought. We then try  to iterate, iterate, iterate to come up with   00:41:14.640 --> 00:41:19.520 something that’s testable. Then we go into a  lab, and we test it. So in that world, right,   00:41:19.520 --> 00:41:23.280 sitting there going, like, “How do I know this  transformer is going to work?” The answer is,   00:41:23.280 --> 00:41:28.400 “For what?” Like, it’s going to work to  make something up … well, guess what? We   00:41:28.400 --> 00:41:34.146 knew early on with LLMs that hallucination was  a feature, not a bug for what we wanted to do.  00:41:34.146 --> 00:41:41.600 LEE: [LAUGHS] So I think that really  touches on just the fact that there's   00:41:41.600 --> 00:41:46.480 so many unknowns and such lack  of precision and exactness in   00:41:46.480 --> 00:41:50.240 our understanding of human biology and  of medicine. Carey, what do you think? 00:41:50.240 --> 00:41:57.280 GOLDBERG: I mean, I just have this emotional  reaction, which is that I love the idea of AI   00:41:57.280 --> 00:42:04.720 marching into biomedical science and everything  from getting to the virtual cell eventually to,   00:42:06.000 --> 00:42:09.040 Zak, I think it was a colleague  of yours who recently published   00:42:09.040 --> 00:42:16.320 about ... it was a new medication that  had been sort of discovered by AI,   00:42:16.320 --> 00:42:20.320 and it was actually testing out up to  the phase II level or something, right? 00:42:20.320 --> 00:42:21.960 KOHANE: Oh, this is Marinka’s work. 00:42:21.960 --> 00:42:29.840 GOLDBERG: Yeah, Marinka, Marinka Zitnik. And …  yeah. So, I mean, I think it avoids a lot of the,   00:42:29.840 --> 00:42:35.200 sort of, dilemmas that are involved with safety  and so on with AI coming into medicine. And it's   00:42:35.200 --> 00:42:40.960 just the discovery process, which we all  want to advance as quickly as possible.   00:42:40.960 --> 00:42:45.760 And it seems like it actually has a great deal of  potential that's already starting to be realized. 00:42:45.760 --> 00:42:48.080 LEE: Oh, absolutely. 00:42:48.640 --> 00:42:54.000 KOHANE: I love this topic. First of all, I  thought, actually, I think Bill and Seb, actually,   00:42:54.000 --> 00:43:00.080 had interesting things to say on that very topic,  rationales which I had not really considered why,   00:43:00.080 --> 00:43:05.520 in fact, things might progress faster in  the discovery space than in the clinical   00:43:05.520 --> 00:43:13.680 delivery space, just because we don't know in  clinical medicine what we're trying to maximize   00:43:13.680 --> 00:43:18.720 precisely. Whereas for a drug effect, we  do know what we're trying to maximize. 00:43:18.720 --> 00:43:22.080 LEE: Well, in fact, I happened  to save that snippet from Bill   00:43:22.080 --> 00:43:26.468 Gates saying that. So let's cue that up. 00:43:26.468 --> 00:43:33.680 BILL GATES: I think it’s very much within  the realm of possibility that the AI is   00:43:33.680 --> 00:43:39.120 not only accelerating healthcare discovery  but substituting for a lot of the roles of,   00:43:39.120 --> 00:43:45.120 you know, “I’m an organic chemist,” or “I run  various types of assays.” I can see those,   00:43:45.120 --> 00:43:50.880 which are, you know, testable-output-type  jobs but with still very high value,   00:43:50.880 --> 00:43:56.120 I can see, you know, some replacement  in those areas before the doctor. 00:43:56.120 --> 00:44:00.200 LEE: So, Zak, isn't that Bill  saying exactly what you’re saying? 00:44:00.200 --> 00:44:05.360 KOHANE: That is my point. I have to say that  this is another great bet; that either we're   00:44:05.360 --> 00:44:09.920 all going to be surprised or a large group  of people will be surprised or disappointed. 00:44:09.920 --> 00:44:14.240 There's still a lot of people in  the sort of medicinal chemist,   00:44:14.240 --> 00:44:18.000 trialist space who are still extremely  skeptical that this is going to work.   00:44:18.000 --> 00:44:23.840 And we haven't quite shown them yet that it is.  Why have we not shown them? Because we haven't   00:44:23.840 --> 00:44:28.880 gone all the way to a phase III study, which  showed that the drug behaves as expected to,   00:44:28.880 --> 00:44:34.640 is effective, and basically doesn't hurt  people. That turns out to require a lot of   00:44:34.640 --> 00:44:39.120 knowledge. I actually think we're getting  there, but I understand the skepticism. 00:44:39.120 --> 00:44:41.720 LEE: Carey, what are your thoughts? 00:44:41.720 --> 00:44:45.920 GOLDBERG: Yeah. I mean, there will be no  way around going through full-on clinical   00:44:45.920 --> 00:44:49.520 trials for anything to ever reach  the market. But at the same time,   00:44:50.800 --> 00:44:56.320 you know, it's clearly very promising. And just  to throw out something for the pure fun of it,   00:44:56.320 --> 00:45:01.360 Peter, I saw … one of my favorite tweets recently  was somebody saying, you know, isn't it funny how   00:45:01.360 --> 00:45:07.760 computer science is actually becoming a lot more  like biology in that it's just becoming empirical. 00:45:07.760 --> 00:45:11.600 It's like you just throw stuff at the AI and  see what it does. [LAUGHTER] And I was like,   00:45:11.600 --> 00:45:15.120 oh, yeah, that's what Peter was  doing when we wrote the book. I mean,   00:45:15.120 --> 00:45:18.720 he understood as many innards as  anybody can. But at the same time,   00:45:18.720 --> 00:45:25.240 it was a totally empirical exercise in seeing what  this thing would do when you threw things at it. 00:45:25.240 --> 00:45:25.920 LEE: Right. 00:45:25.920 --> 00:45:28.389 GOLDBERG: So it's the new biology. 00:45:28.389 --> 00:45:34.320

LEE: Well, yeah. So I think we talked in  our book about accelerating, you know,   00:45:34.320 --> 00:45:41.520 biomedical knowledge and medical science.  And that actually seems to be happening. And   00:45:42.080 --> 00:45:46.320 I really had fun talking to  Daphne Koller about some of   00:45:46.320 --> 00:45:51.200 the accomplishments that she's made. And  so here's a little snippet from Daphne. 00:45:51.200 --> 00:45:56.240 DAPHNE KOLLER: This will impact not only the  early stages of which hypotheses we interrogate,   00:45:56.240 --> 00:46:00.400 which molecules we move forward, but also  hopefully at the end of the day, which molecule   00:46:00.400 --> 00:46:06.240 we prescribe to which patient. And I think there’s  been obviously so much narrative over the years   00:46:06.240 --> 00:46:12.080 about precision medicine, personalized medicine,  and very little of that has come to fruition, with   00:46:12.080 --> 00:46:17.520 the exception of, you know, certain islands in  oncology, primarily on genetically driven cancers. 00:46:17.520 --> 00:46:23.600 LEE: So, Zak, when I was listening to that,  I was reminded of one of the very first   00:46:23.600 --> 00:46:29.680 examples that you had where, you know,  you had a very rare case of a patient,   00:46:29.680 --> 00:46:38.480 and you're having to narrow down some pretty  complex and very rare genetic conditions.   00:46:38.480 --> 00:46:43.920 This thing that Daphne says, that seems to  be the logical conclusion that everyone who's   00:46:43.920 --> 00:46:51.160 thinking hard about AI and biology is coming  to. Does it seem more real now two years on? 00:46:51.160 --> 00:46:57.680 KOHANE: It absolutely seems more real. Here's  some sad facts. If you are at a cancer center,   00:46:57.680 --> 00:47:04.640 you will get targeted therapies if you qualify  for it. Outside cancer centers, you won't. And   00:47:04.640 --> 00:47:08.960 it's not that the therapies aren't available.  It's just that you won't have people thinking   00:47:08.960 --> 00:47:14.240 about it in that way. And especially if you have  some of the rare and more aggressive cancers,   00:47:14.240 --> 00:47:20.320 if you're outside one of those cancer centers,  you're at a significant disadvantage for   00:47:20.960 --> 00:47:27.840 survival for that reason. And so anything  that provides just the “simple,” in quotes,   00:47:28.400 --> 00:47:35.600 dogged investigation of the targeted  therapies for patients, it's a home run. 00:47:36.160 --> 00:47:47.280 So my late graduate student, Atul Butte, died  recently at UCSF, where he was both a professor   00:47:47.280 --> 00:47:53.200 and the leader of the Bakar Institute, and he  was a Zuckerberg Chan Professor of Pediatrics. 00:47:53.200 --> 00:48:00.960 He was diagnosed with a rare tumor two years  ago. His wife is a PhD biologist, and when he   00:48:00.960 --> 00:48:07.520 was first diagnosed, she sent me the diagnosis and  the mutations. And I don't know if you know this,   00:48:07.520 --> 00:48:11.200 Peter, but this was still when we were writing  the book and people didn't know about GPT-4. 00:48:11.200 --> 00:48:17.600 I put in those mutations into GPT-4  and the diagnosis. And I said, “I'd   00:48:17.600 --> 00:48:24.720 like to help treat my friend. What's the right  treatment?” And GPT, to paraphrase, GPT-4 said,   00:48:24.720 --> 00:48:28.560 “Before we start talking about treatment, are  you sure this is the right diagnosis? Those   00:48:28.560 --> 00:48:32.640 mutations are not characteristic  for that tumor.” And he had been   00:48:32.640 --> 00:48:39.120 misdiagnosed. And then they changed the  diagnosis therapy and some personnel. 00:48:39.120 --> 00:48:45.680 So I don't have to hallucinate this. It's  already happened, and we're going to need   00:48:45.680 --> 00:48:54.080 this. And so I think targeted therapy for  cancers is the most obvious use. And if God   00:48:54.080 --> 00:49:00.160 forbid one of you has a family member who  has cancer, it's moral malpractice not to   00:49:00.160 --> 00:49:05.100 look at the genetics and run it past GPT-4  and say, “What are the available therapies?” 00:49:05.100 --> 00:49:07.600 LEE: Yeah. KOHANE: I really deeply believe that. 00:49:07.600 --> 00:49:11.600 LEE: Carey, I think one thing you've  always said is that you're surprised   00:49:11.600 --> 00:49:18.400 that we don't hear more stories along  these lines. And I think you threw a   00:49:18.400 --> 00:49:21.600 quote from Mustafa Suleyman back  at me. Do you want to share that? 00:49:21.600 --> 00:49:26.720 GOLDBERG: Yes. Recently, I believe  it was a Big Technology interview,   00:49:26.720 --> 00:49:32.720 and the reporter asked Mustafa Suleyman,  “So you guys are seeing 50 million queries,   00:49:32.720 --> 00:49:37.200 medical queries, a day [to Copilot and  Bing]. You know, how's that going?” And   00:49:37.840 --> 00:49:45.040 I think I am a bit surprised that we're  not seeing more stories of all types. Both   00:49:45.040 --> 00:49:52.000 here's how it helped me and also here was maybe,  you know, a suggestion that was not optimal. 00:49:52.000 --> 00:49:58.320 LEE: Yeah. I do think in our book, we  did predict both positive and negative   00:49:58.320 --> 00:50:05.920 outcomes of this. And it is odd. Atul was  very open with his story. And of course,   00:50:05.920 --> 00:50:11.040 he is such … he was such a prominent  leader in the world of medicine. 00:50:11.040 --> 00:50:19.120 But I think I share your surprise, Carey. I  expected by now that a lot more public stories   00:50:19.120 --> 00:50:22.840 would be out. Maybe there is someone writing  a book collecting these things, I don't know. 00:50:22.840 --> 00:50:26.630 KOHANE: Maybe someone called Carey  Goldberg should write that book. [LAUGHTER] 00:50:26.630 --> 00:50:29.760 GOLDBERG: Write a book, maybe.  I mean, we have Patients Use AI,   00:50:29.760 --> 00:50:34.000 which

is a wonderful blog by Dave  deBronkart, the patient advocate. 00:50:34.000 --> 00:50:38.240 But I wonder if it's also something  structural, like who would be or what   00:50:38.240 --> 00:50:42.560 would be the institution that would be  gathering these stories? I don’t know. 00:50:42.560 --> 00:50:43.108 LEE: Right. 00:50:43.108 --> 00:50:46.160 KOHANE: And that's the problem. You see, this goes  back to the same problem that [Ethan] Mollick was   00:50:46.160 --> 00:50:52.560 talking about. Individual doctors are using them.  The hospital as a whole is not doing that. So it's   00:50:52.560 --> 00:50:58.960 not judging the quality, as part of its quality  metrics, of how good the AI is performing and   00:50:58.960 --> 00:51:05.840 what new has happened. And the other audience,  namely the patients, have no mechanism. There   00:51:05.840 --> 00:51:11.680 is no mechanism to go to Better Business Bureau  and say, “They screwed up,” or “This was great.” 00:51:11.680 --> 00:51:17.840 LEE: So now I want to get a little more  futuristic. And this gets into whether   00:51:17.840 --> 00:51:24.240 AI is really going to get almost to the ab  initio understanding of human biology. And   00:51:24.240 --> 00:51:30.880 so Eric Topol, who is one of the guests,  spoke to this a bit. So let's hear this. 00:51:30.880 --> 00:51:36.400 LEE: So you talk about a virtual cell.  Is that achievable within 10 years,   00:51:36.400 --> 00:51:38.960 or is that still too far out?  00:51:38.960 --> 00:51:45.040 ERIC TOPOL: No, I think within 10 years for sure.  You know, the group that got assembled, that Steve   00:51:45.040 --> 00:51:50.720 Quake pulled together, I think has 42 authors in  a paper in Cell. The fact that he could get these   00:51:50.720 --> 00:51:58.560 42 experts in life science and some in computer  science to come together and all agree that not   00:51:58.560 --> 00:52:04.160 only is this a worthy goal, but it’s actually  going to be realized, that was impressive. 00:52:04.160 --> 00:52:10.080 LEE: You know, I have to say Eric's optimism  took me aback. Just speaking as a techie,   00:52:10.080 --> 00:52:16.320 I think I started off being optimistic: as  soon as we can figure out molecular dynamics,   00:52:16.320 --> 00:52:22.240 biology can be solved. And then you  start to learn more about biochemistry,   00:52:22.240 --> 00:52:30.560 about the human cell, and then you realize, oh,  my God, this is just so vast and unknowable.   00:52:30.560 --> 00:52:34.880 And now you have Eric Topol saying,  “Well, in less than 10 years.” 00:52:34.880 --> 00:52:41.840 KOHANE: So what's delightful about this period  is that those of us who are cautious were so   00:52:41.840 --> 00:52:48.000 incredibly wrong about AI two years ago.  [LAUGHTER] That's a true joy ... I mean,   00:52:48.000 --> 00:52:55.040 absolute joy. It's great to have your  futurism made much more positive. 00:52:56.000 --> 00:53:02.640 But I think that we're going  from, you know, for example,   00:53:02.640 --> 00:53:08.000 AlphaFold has had tremendous impact.  But remember, that was built on years   00:53:08.000 --> 00:53:12.640 of acquisition of crystallography data  that was annotated. And of course,   00:53:12.640 --> 00:53:16.960 the annotation process becomes less relevant as  you go down the pipe, but it started from that. 00:53:16.960 --> 00:53:17.440 LEE: Yes. 00:53:17.440 --> 00:53:21.600 KOHANE: And there's lots of parts of the  cell. So when people talk about virtual   00:53:21.600 --> 00:53:26.240 cells—I don't mean to get too technical—mostly  they're talking about perturbation of gene   00:53:26.240 --> 00:53:32.880 expression. They're not talking about,  “Oh, this is how the liposome and the   00:53:34.320 --> 00:53:37.760 centrosome interact, and notice how  the Golgi bodies bump into each other.” 00:53:37.760 --> 00:53:41.600 There's a whole bunch of other levels of  abstraction we know nothing about. This   00:53:41.600 --> 00:53:48.560 is a complex factory. And right now, we're  sort of the level from code into loading code   00:53:48.560 --> 00:53:53.200 into memory. We're not talking about how  the rest of the robots work in that cell,   00:53:53.200 --> 00:53:57.760 and how the rest of those robots work in the cell  turns out to be pretty important to functioning. 00:53:57.760 --> 00:54:06.400 So I'd love to be wrong again. And in 10 years, oh  yeah, not only, you know, our first in-human study   00:54:06.400 --> 00:54:12.880 will be you, Dr. Zak. We're going put the drug  because we fully simulated you. That'd be great. 00:54:12.880 --> 00:54:13.992 LEE: Yes. 00:54:13.992 --> 00:54:17.040 KOHANE: And, by the way, just to give  people their due, there probably was a   00:54:17.040 --> 00:54:22.000 lot of animal research that could be done  in silico and that for various political   00:54:22.000 --> 00:54:29.520 reasons we're now seeing happen. That's a  good thing. But I think that sometimes it   00:54:29.520 --> 00:54:36.080 takes a lot of hubris to get us where we need  to get, but my horizon is not the same as his. 00:54:36.080 --> 00:54:43.200 LEE: So I guess I have to take this time to brag.  Just recently out of our AI for Science team did   00:54:43.200 --> 00:54:55.360 publish in Science a biological emulator that  does pretty long timespan, very, very precise,   00:54:55.360 --> 00:55:00.560 and very efficient molecular dynamics,  biomolecular dynamics emulation. We call it   00:55:00.560 --> 00:55:05.592 emulation because it's not simulating every single  time step but giving you the final confirmations. 00:55:05.592 --> 00:55:07.661 KOHANE: That's an amazing result. 00:55:07.661 --> 00:55:07.672 LEE: Yeah. 00:55:07.672 --> 00:55:10.880 KOHANE: But … that is an amazing  result. And you're doing it in   00:55:10.880 --> 00:55:14.320 some very important interactions.  But there's so much more to do. 00:55:14.320 --> 00:55:17.440 LEE: I know,

and it's single  molecules; it’s not even two   00:55:17.440 --> 00:55:24.320 molecules. There's so much more to  go for here. But on the other hand,   00:55:24.880 --> 00:55:34.040 Eric is right, you know, 42 experts writing  for Cell, you know, that's not a small matter. 00:55:34.040 --> 00:55:37.680 KOHANE: So I think sometimes you  really need to drink your own   00:55:38.720 --> 00:55:45.520 hallucinogens to actually succeed. Because  remember, when the Human Genome Project was   00:55:46.800 --> 00:55:49.520 launched, we didn't know how to sequence at scale. 00:55:49.520 --> 00:55:53.360 We said maybe we would get there. And  then in order to get the right funding   00:55:53.360 --> 00:55:59.360 and excitement and, I think, focus,  we predicted that by early 2000s we'd   00:55:59.360 --> 00:56:04.720 be transforming medicine. Has not  happened yet. Things have happened,   00:56:04.720 --> 00:56:12.080 but at a much slower pace. And we're 25 years  out. In fact, we're 35 years out from the launch. 00:56:13.040 --> 00:56:15.440 But again, things are getting faster and faster.   00:56:15.440 --> 00:56:20.400 Maybe the singularity is going to make a whole  bunch of things easier. And GPT-6 will just say,   00:56:20.400 --> 00:56:25.024 “Zak, you are such a pessimist.  Let me show you how it's done.” 00:56:25.024 --> 00:56:25.040 GOLDBERG: Yeah. 00:56:25.040 --> 00:56:28.240 It really is a pessimism  versus optimism. Like is it,   00:56:28.240 --> 00:56:33.280 I mean, biology is such a bitch, right.  [LAUGHTER] Can we actually get there? 00:56:33.280 --> 00:56:38.400 At the same time, everyone was surprised and  blown away by the, you know, the quantum leap   00:56:38.400 --> 00:56:44.080 of GPT-4. Who knows when enough data gets in  there if we might not have a similar leap. 00:56:44.080 --> 00:56:45.280 LEE: Yeah. All right. 00:56:45.280 --> 00:56:51.920 So let's get back to healthcare delivery.  Besides Morgan Cheatham, we talked to [a]   00:56:51.920 --> 00:56:57.920 more junior medical student who's at the Kaiser  Permanente School of Medicine, Daniel Chen. And,   00:56:57.920 --> 00:57:03.040 you know, I asked him about this question  of patients who come in armed [LAUGHS] with   00:57:03.040 --> 00:57:08.240 a lot of their own information.  Let's hear what he said about this. 00:57:08.240 --> 00:57:12.320 DANIEL CHEN: But for those that come in  with a list, I sometimes sit down with them,   00:57:12.320 --> 00:57:17.200 and we’ll have a discussion, honestly. …  “I don’t think you have meningitis because,   00:57:17.200 --> 00:57:21.520 you know, you’re not having a fever. Some of  the physical exam maneuvers we did were also   00:57:21.520 --> 00:57:26.480 negative. So I don’t think you have anything  to worry about that,” you know. So I think   00:57:26.480 --> 00:57:32.240 it’s having that very candid conversation with  the patient that helps build that initial trust. 00:57:32.240 --> 00:57:37.760 LEE: So, Zak, as far as I can tell, Daniel  and Morgan are figuring this out on their own   00:57:37.760 --> 00:57:42.920 as medical students. I don't think this is  part of the curriculum. Does it need to be? 00:57:42.920 --> 00:57:48.720 KOHANE: It's missing the bigger point. The  incentives and economic forces are such that   00:57:48.720 --> 00:57:58.240 even if you were Daniel, and things have not  changed in terms of incentives, and it's 2030,   00:57:58.240 --> 00:58:02.000 he still has to see this many patients in an hour. 00:58:02.000 --> 00:58:09.280 And sitting down, going over that with a patient,  let's say some might need more ... in fact,   00:58:09.280 --> 00:58:13.520 I think computer scientists are enriched for  these sort of neurotic “explain [to] me why   00:58:13.520 --> 00:58:18.320 this works,” when often the answer is,  “I have no idea; empirically it does.” 00:58:19.520 --> 00:58:26.880 And patients in some sense deserve that  conversation, and we're taught about joint   00:58:26.880 --> 00:58:32.800 decision making, but in practice, there's a  lot of skills that are deployed to actually   00:58:32.800 --> 00:58:39.840 deflect so that you can get through the  appointment and see enough patients per hour. 00:58:40.800 --> 00:58:46.560 And that's why I think that one of the  central … another task for AI is how to   00:58:46.560 --> 00:58:51.120 engage with patients to actually  explain to them why their doctor   00:58:51.120 --> 00:58:55.680 is doing what he's doing and perhaps ask  the one or two questions that you should   00:58:55.680 --> 00:59:00.061 be asking the doctor in order to reassure  you that they're doing the right thing. 00:59:00.061 --> 00:59:00.072 LEE: Yeah. 00:59:00.072 --> 00:59:05.920 KOHANE: I just … right now, we are going to  have less doctor time, not more doctor time. 00:59:05.920 --> 00:59:14.480 And so I've always been struck by the  divide between medicine that we're taught   00:59:14.480 --> 00:59:24.000 as it should be practiced as a gentle person's  vocation or sport as opposed to assembly line,   00:59:24.000 --> 00:59:27.920 heads down “you've got to see those  patients by the end of the day” because,   00:59:27.920 --> 00:59:30.560 otherwise, you haven't seen all  the patients at the end of the day. 00:59:30.560 --> 00:59:35.520 LEE: Yeah. Carey, I've been dying to ask  you this, and I have not asked you this   00:59:35.520 --> 00:59:42.720 before. When you go see a doctor, are you  coming in armed with ChatGPT information? 00:59:42.720 --> 00:59:48.080 GOLDBERG: I haven't needed to yet, but  I certainly would. And also my reaction   00:59:49.680 --> 00:59:57.200 to the medical student description was, I think  we need to distinguish between the last 20 years,   00:59:57.200 --> 01:00:03.200 when patients would come in armed with Google, and  what they're coming in with now because at least   01:00:03.200 --> 01:00:12.320 the experiences that I've witnessed, it is miles 

better to have gone back and forth with GPT-4 than   01:00:12.320 --> 01:00:20.880 with, you know, dredging what you can from Google.  And so I think we should make that distinction. 01:00:20.880 --> 01:00:25.520 And also, the other thing that most  interested me was this question for   01:00:25.520 --> 01:00:32.696 medical students of whether they should not  use AI for a while so that they can learn … 01:00:32.696 --> 01:00:32.706 LEE: Yes. 01:00:32.706 --> 01:00:37.120 GOLDBERG: … how to think and similarly maybe  don't use the automated scribes for a while   01:00:37.120 --> 01:00:42.000 so they can learn how to do a note. And at  what point should they then start being able   01:00:42.000 --> 01:00:47.920 to use AI? And I suspect it's fairly early  on that, in fact, they're going be using it   01:00:47.920 --> 01:00:53.400 so consistently that there's not that much they  need to learn before they start using the tools. 01:00:53.400 --> 01:00:59.520 LEE: These two students were incredibly  impressive. And so I have wondered, you know,   01:00:59.520 --> 01:01:04.800 if we got a skewed view of things. I mean, Morgan  is, of course, a very, very impressive person.   01:01:04.800 --> 01:01:12.880 And Daniel was handpicked by the dean of the  medical school to be a subject of this interview. 01:01:12.880 --> 01:01:18.000 KOHANE: You know, we filter our students, by and  large, I mean, there's exceptions, but students in   01:01:18.000 --> 01:01:23.840 medical school are so starry eyed. And they are  really ... they got into medical school—I mean,   01:01:23.840 --> 01:01:27.040 some of them may have faked it—but a lot of  them because they really wanted to do good. 01:01:27.040 --> 01:01:27.480 LEE: Right. 01:01:27.480 --> 01:01:30.640 KOHANE: And they really wanted to  help. And so this is very constant   01:01:30.640 --> 01:01:35.440 with them. And it's only when they're  in the machine, past medical school,   01:01:35.440 --> 01:01:39.360 that they realize, oh my God, this  is a very, very different story. 01:01:39.360 --> 01:01:43.520 And I can tell you, because I teach a  course in computational-enabled medicine,   01:01:43.520 --> 01:01:49.280 so I get a lot of these nerd medical students,  and I'm telling them, “You're going to experience   01:01:49.280 --> 01:01:53.200 this. And you're going to say, ‘I'm not going  to able to change medicine until I get enough   01:01:53.200 --> 01:01:58.960 cred 10, 15 years from now, whereas I could start  my own company and immediately change medicine.’” 01:01:58.960 --> 01:02:03.120 And increasingly I'm getting calls  in like residency and saying, “Zak,   01:02:03.120 --> 01:02:05.025 help me. How do I get out of this?” 01:02:05.025 --> 01:02:05.040 GOLDBERG: Wow. 01:02:05.040 --> 01:02:10.240 KOHANE: And so I think there's  a real disillusionment of, like,   01:02:10.240 --> 01:02:13.920 between what we're asking for people  coming to medical school—we're looking   01:02:13.920 --> 01:02:21.280 for a phenotype—and then we're disappointing  them massively, not everywhere, but massively. 01:02:21.280 --> 01:02:29.760 And for me, it's very sad because among  our best and brightest, and then because   01:02:29.760 --> 01:02:38.080 of economics and expectations and the nature  of the beast, they're not getting to enjoy the   01:02:38.080 --> 01:02:44.000 most precious part of being a doctor, which is  that real human connection, and longitudinality,   01:02:44.000 --> 01:02:49.200 you know, the connection between the same doctor  visit after visit, is more and more of a luxury. 01:02:49.200 --> 01:02:55.760 LEE: Well, maybe this gets us to the last  episode, you know, where I talk to a former,   01:02:55.760 --> 01:03:01.600 you know, state director of public health,  Umair Shah, and with Gianrico Farrugia,   01:03:01.600 --> 01:03:08.320 who's the CEO of Mayo Clinic. And I think  if there's one theme that I took away from   01:03:08.320 --> 01:03:14.160 those conversations is that we're not  thinking broadly enough nor big enough. 01:03:14.720 --> 01:03:23.200 And so here's a little quote of exchange that  Umair Shah, who was the former head of public   01:03:23.200 --> 01:03:29.360 health in the State of Washington and prior  to that in Harris County, Texas, and we had   01:03:29.360 --> 01:03:35.520 a conversation about what techies tend to focus  on when they're thinking about AI and medicine. 01:03:35.520 --> 01:03:41.120 UMAIR SHAH: I think one of the real  challenges is that when even tech companies,   01:03:41.120 --> 01:03:46.560 and you can name all of them, when they  look at what they're doing in the AI space,   01:03:46.560 --> 01:03:49.800 they gravitate towards healthcare delivery. 01:03:49.800 --> 01:03:54.960 LEE: Yes. And in fact, it's not even  delivery. I think techies—I did this,   01:03:54.960 --> 01:04:00.000 too—tend to gravitate specifically to diagnosis. 01:04:00.000 --> 01:04:03.520 LEE: I have been definitely guilty. I think Umair,   01:04:03.520 --> 01:04:08.480 of course, was speaking as a former  frustrated public health official   01:04:08.480 --> 01:04:15.040 in just thinking about all the other things that  are important to maintain a healthy population. 01:04:15.040 --> 01:04:18.080 Is there some lesson that we should take away? I   01:04:18.080 --> 01:04:22.480 think our book also focused a  lot on things like diagnosis. 01:04:22.480 --> 01:04:27.440 KOHANE: Yeah. Well, first of all, I  think we just have to have humility. And   01:04:27.440 --> 01:04:36.960 I think it's a really important ingredient. I  found myself staring at the increase in lifespan   01:04:36.960 --> 01:04:42.640 in human beings over the last two centuries  and looking for bumps that were attributable. 01:04:42.640 --> 01:04:45.600 I'm in medical school. I've already made this  major commitment. What are the bumps that   01:04:45.600 --> 01:04:49.200 are attributable to

medicine? And there  was one bump that was due to vaccines,   01:04:49.200 --> 01:04:57.440 a small bump. Another small bump that was due  to antibiotics. And the rest of it is nutrition,   01:04:57.440 --> 01:05:01.040 sanitation, yeah, nutrition and sanitation. 01:05:01.040 --> 01:05:09.440 And so I think doctors can be incredibly  valuable, but not all the time. And we're   01:05:09.440 --> 01:05:16.800 spending now one-sixth of our GDP on it. The  majority of it is not effectively prolonging   01:05:16.800 --> 01:05:22.160 life. And so the humility has to be  the right medicine at the right time. 01:05:22.160 --> 01:05:26.320 But that runs, (A) against a bunch of  business models. It runs against the   01:05:26.320 --> 01:05:32.640 primacy of doctors in healthcare. It was  one thing when there were no textbooks;   01:05:33.440 --> 01:05:38.160 there was no PubMed. You know, the doctor  was the repository of all the probably   01:05:38.160 --> 01:05:45.280 knowledge that we have. But I think your  guests were right. We have to think more   01:05:45.280 --> 01:05:51.440 broadly in the public health way. How do we  make knowledge pervasive like sanitation? 01:05:51.440 --> 01:05:56.960 GOLDBERG: Although I would add that  since what we're talking about is AI,   01:05:57.520 --> 01:06:01.280 it's harder to see if ... and if what  you're talking about is public health,   01:06:01.280 --> 01:06:07.120 I mean, it was certainly very important to have  good data during the pandemic, for example. 01:06:07.120 --> 01:06:11.600 But most of the ways to improve public health,  like getting people to stop smoking and eat   01:06:11.600 --> 01:06:17.280 better and sleep better and exercise more,  are not things that AI can help with that   01:06:17.280 --> 01:06:25.840 much. Whereas diagnosis or trying to improve  treatment are places that it could tackle. 01:06:25.840 --> 01:06:27.440 And in fact, Peter, I wanted to put you—oh,   01:06:27.440 --> 01:06:30.640 wait, Zak's going to say something—but,  Peter, I wanted to put you on the spot. 01:06:30.640 --> 01:06:30.790 LEE: Yeah. 01:06:30.790 --> 01:06:36.880 GOLDBERG: I mean, if you had a medical  issue now, and you went to a physician,   01:06:36.880 --> 01:06:40.800 would you be OK with them not using generative AI? 01:06:40.800 --> 01:06:45.840 LEE: I think if it's a complex or a mysterious  case, I would want them to use generative   01:06:45.840 --> 01:06:53.760 AI. I would want that second opinion on  things. And I would personally be using it.   01:06:56.240 --> 01:07:02.240 If for no other reason than just to  understand what the chart is saying. 01:07:02.240 --> 01:07:06.800 I don't see, you know, how or  why one wouldn't do that now. 01:07:06.800 --> 01:07:13.440 KOHANE: It's such a cheap second opinion,  and people are making mistakes. And even if   01:07:13.440 --> 01:07:18.640 there are mistakes on the part of AI,  if there's a collision, discrepancy,   01:07:18.640

--> 01:07:22.080 that's worth having a discussion. And  again, this is something that we used   01:07:22.080 --> 01:07:26.789 to do more of when we had more time with  the patients; we'd have clinic conferences. 01:07:26.789 --> 01:07:26.800 LEE: Yeah. 01:07:26.800 --> 01:07:35.280 KOHANE: And we don't have that now. So I do think  that there is a role for AI. But I think again,   01:07:35.280 --> 01:07:42.640 it's much more of a continual presence, being part  of a continued conversation rather than an oracle. 01:07:43.200 --> 01:07:47.840 And I think that's when you'll start  seeing, when the AI is truly a colleague,   01:07:47.840 --> 01:07:51.280 and saying, “You know, Zak, that's  the second time you made that mistake.   01:07:52.160 --> 01:07:55.840 You know, that's not obesity. That's  the effect of your drugs that you're   01:07:55.840 --> 01:08:01.440 giving her. You better back off of it.”  And that's what we need to see happen. 01:08:01.440 --> 01:08:06.480 LEE: Well, and for the business of healthcare,  that also relates directly to quality scores,   01:08:06.480 --> 01:08:11.200 which translates into money  for healthcare providers. 01:08:11.920 --> 01:08:16.880 So the last person that we interviewed  was Gianrico Farrugia. And, you know,   01:08:16.880 --> 01:08:21.280 I was sort of wondering, I was expecting  to get a story from a CEO saying, “Oh,   01:08:21.280 --> 01:08:27.280 my God, this has been so disruptive, incredibly  important, meaningful, but wow, what a headache.” 01:08:28.320 --> 01:08:34.301 At least Gianrico didn't expose any of that.  Here's one of the snippets to give you a sense. 01:08:34.301 --> 01:08:38.640 GIANRICO FARRUGIA: When generative AI came, for  us, it's like, I wouldn't say we told you so,   01:08:38.640 --> 01:08:43.280 but it's like, ah, there you go. Here's another  tool. This is what we've been talking about. Now   01:08:43.280 --> 01:08:47.760 we can do it even better. Now we can move  even faster. Now we can do more for our   01:08:47.760 --> 01:08:54.560 patients. It truly never was disruptive. It truly  immediately became enabling, which is strange,   01:08:54.560 --> 01:09:00.280 right, because something as disruptive as  that instantly became enabling at Mayo Clinic. 01:09:00.280 --> 01:09:08.560 LEE: So I tried pretty hard in that interview to  get Gianrico to admit that there was a period of   01:09:08.560 --> 01:09:16.480 headache and disruption here. And he never, ever  gave me that. And so I take him at his word. 01:09:16.480 --> 01:09:21.480 Zak, maybe I should ask you, what about Harvard  and the whole Harvard medical ecosystem? 01:09:21.480 --> 01:09:30.000 KOHANE: I would be surprised if there are  system-wide measurable gains in health quality   01:09:30.000 --> 01:09:38.000 right now from AI. And I do have to say that  Mayo is one of the most marvelous organizations   01:09:38.000 --> 01:09:45.440 in terms of team behavior. So if there's someone  who's gotten the team part of it right, they've   01:09:45.440 --> 01:09:50.058 come the closest, which relates to our prior  conversation. They have the quarterback idea … 01:09:50.058 --> 01:09:52.880 LEE: Yes. KOHANE: … pretty well down compared to others. 01:09:52.880 --> 01:10:01.280 Nonetheless, I take him at his word, that  it hasn't disrupted them. But I'm also,   01:10:01.280 --> 01:10:07.440 I have yet to see the evidence that there's  been a quantum leap in quality or efficacy.   01:10:07.440 --> 01:10:16.080 And I do believe that it's possible to have a  quantum leap in efficacy in the right system. 01:10:16.080 --> 01:10:22.800 So if they haven't been disrupted, I  would venture that they've absorbed it,   01:10:22.800 --> 01:10:30.800 but they haven't used it to its fullest potential.  And the way I could be proven wrong is next year,   01:10:31.520 --> 01:10:35.040 also the metrics showing that over  the last year, they've had, you know,   01:10:35.040 --> 01:10:38.080 decreased readmissions, decreased complications,   01:10:38.080 --> 01:10:43.040 decreased errors and all that. And if so, God  bless them. And we should all be more like Mayo. 01:10:43.040 --> 01:10:50.240 LEE: So I thought a little bit about  two other quotes from the interviews   01:10:50.240 --> 01:10:57.600 that sort of maybe would send us  off with some more inspirational   01:10:58.320 --> 01:11:04.560 kind of view of the future. And so there's one  from Bill Gates and one from Gianrico Farrugia.   01:11:04.560 --> 01:11:12.320 So what I'd like to do is to play both of those  and then maybe we can have our last comments. 01:11:12.320 --> 01:11:18.240 BILL GATES: You know, I’ve gone so far as to  tell politicians with national health systems   01:11:18.240 --> 01:11:27.440 that if they deploy AI appropriately, that the  quality of care, the overload of the doctors,   01:11:27.440 --> 01:11:33.120 the improvement in the economics will be enough  that their voters will be stunned because they   01:11:33.120 --> 01:11:39.760 just don’t expect this, and, you know, they could  be reelected just on this one thing of fixing   01:11:39.760 --> 01:11:46.720 what is a very overloaded and economically  challenged health system in these rich countries. 01:11:46.720 --> 01:11:48.701 And now Gianrico. 01:11:48.701 --> 01:11:52.800 GIANRICO FARRUGIA: And we seemed to be on a  linear path, which is, let's try and reduce   01:11:52.800 --> 01:12:00.160 administrative burden. Let's try and truly be a  companion to a physician or other provider. … And   01:12:00.160 --> 01:12:04.720 then in the next step, we keep going until  we get to, now we can call it agentic AI,   01:12:04.720 --> 01:12:11.840 whatever we want to talk about. And my view  was, no, is that let's start with that aim,   01:12:11.840 --> 01:12:17.440 the last aim … because the others will come  automatically if you're working on that harder   01:12:17.440 --> 01:12:23.280 problem. Because one,

to get to that harder  problem, you'll find all the other solutions. 01:12:23.280 --> 01:12:33.440 All right. I think these are both kind of calls  to be more assertive about this and more forward   01:12:33.440 --> 01:12:43.120 leaning. I think two years into the GPT-4 era,  those are pretty significant and pretty optimistic   01:12:43.120 --> 01:12:49.280 calls to action. So maybe just to give  you both one last word. What would be   01:12:49.280 --> 01:12:54.080 one hope that you would have for the world of  healthcare and medicine two years from now? 01:12:54.080 --> 01:13:03.440 KOHANE: I would hope for businesses that whoever  actually owns them at some holding company level,   01:13:03.440 --> 01:13:09.840 regardless of who owns them, are  truly patient-focused companies,   01:13:09.840 --> 01:13:14.240 companies where the whole AI  is about improving your care,   01:13:14.240 --> 01:13:20.000 and it's only trying to maximize your care and  it doesn't care about resource limitations. 01:13:20.000 --> 01:13:26.800 And as I was listening to Bill, and the problem  with what he was saying about saving dollars for   01:13:27.440 --> 01:13:36.400 governments is for many things, we have some very  expensive things that work. And if the AI says,   01:13:36.400 --> 01:13:42.400 “This is the best thing,” it’s going  to break your bank. And instead,   01:13:42.400 --> 01:13:48.400 because of research limitations, we play a  human-based fancy footwork to get out of it. 01:13:48.400 --> 01:13:52.480 That's a hard game to play, and I leave it  to the politicians and the public health   01:13:52.480 --> 01:13:55.360 officials who have to do  those trades of utilities. 01:13:55.360 --> 01:14:03.840 In my role as doctor and patient, I'd like  to see very informed, authoritative agents   01:14:03.840 --> 01:14:10.880 acting only on our behalf so that when we go  and we seek to have our maladies addressed,   01:14:10.880 --> 01:14:18.080 the only issue is, what's the best and  right thing for me now? And I think that   01:14:18.080 --> 01:14:24.080 is both technically realizable. And even in  our weird system, there are business plans   01:14:24.080 --> 01:14:28.280 that will work that can achieve that.  That's my hope for two years from now. 01:14:28.280 --> 01:14:29.590 LEE: Yeah, fantastic. Carey. 01:14:29.590 --> 01:14:35.440 GOLDBERG: Yeah. I second that so  enthusiastically. And I think, you know,   01:14:35.440 --> 01:14:42.800 we have this very glass half full/glass half empty  phenomenon two years after the book came out. 01:14:42.800 --> 01:14:48.960 And it's certainly very nice to see, you know,  new approaches to administrative complexity and   01:14:48.960 --> 01:14:57.440 to prior authorization and all kinds of ways  to make physicians' lives easier. But really   01:14:57.440 --> 01:15:04.000 what we all care about is our own health and  that we would like to be able to optimize the   01:15:04.000 --> 01:15:10.240 use of this truly glorious technological  achievement to be able to live longer   01:15:10.240 --> 01:15:17.736 and better lives. And I think what Zak just  described is the most logical way to do that. 01:15:17.736 --> 01:15:17.755 [TRANSITION MUSIC] 01:15:17.755 --> 01:15:23.040 LEE: Yeah, I think for me, two years  from now, I would like to see all of   01:15:23.040 --> 01:15:30.720 this digital data that's been so painful, such  a burden on every doctor and nurse to record,   01:15:30.720 --> 01:15:38.525 actually amount to something meaningful in the  care of patients. And I think it's possible. 01:15:38.525 --> 01:15:38.555 KOHANE: Amen. GOLDBERG: Yeah. 01:15:38.555 --> 01:15:41.760 LEE: All right, so it's been quite a journey.   01:15:42.320 --> 01:15:46.560 We were joking before we're still on speaking  terms after having written a book. [LAUGHS] 01:15:46.560 --> 01:15:51.920 And then, um, I think listeners might  enjoy knowing that we debated amongst   01:15:51.920 --> 01:15:55.520 ourselves what to do about a second edition,   01:15:55.520 --> 01:15:59.680 which seemed too painful to me, and so I  suggested the podcast, which seemed too   01:15:59.680 --> 01:16:04.640 painful to the two of you [LAUGHTER]. And in the  end, I don't know what would have been easier,   01:16:04.640 --> 01:16:10.000 writing a book or doing this podcast series,  but I do think that we learned a lot. 01:16:10.000 --> 01:16:14.880 Now, last bit of business here. To avoid having   01:16:14.880 --> 01:16:21.200 the three of us try to write a book again and  do this podcast, I leaned on the production   01:16:21.200 --> 01:16:25.680 team in Microsoft Research and the Microsoft  Research Podcast. And I thought it would be   01:16:25.680 --> 01:16:30.400 good to give an explicit acknowledgment to  all the people who've contributed to this. 01:16:30.400 --> 01:16:34.160 So it's a long list of names. I'm going  to read through them all. And then   01:16:34.160 --> 01:16:40.320 I suggest that we all give an applaud  [LAUGHTER] to them. And so here we go.