Pod Insights a16z Podcast a16z Podcast: AI and Your Doctor, Today and Tomorrow About this episode with Eric Topol (@EricTopol) and Vijay Pande (@vijaypande)
Artificial intelligence is coming to the doctor’s office. In this episode, Dr. Eric Topol, cardiologist and chair of innovative medicine at Scripps Research, and a16z’s general partner on th...
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Hanne Tidnam? Hi and welcome to the A16Z Podcast. I'm Hannah. This episode is all about how artificial intelligence is coming to the doctor's office, how it will impact the nature of doctor-patient interactions, diagnosis, prevention, prediction, and everything in between. The conversation between A16Z's general partner Vijay Pandey and Dr. Eric Topol, cardiologist and chair of innovative medicine at Scripps Research, is based on Topol's book, Deep Medicine, and touches on everything from how AI's deep phenotyping can shift our thinking from population health to understanding the medical health essence of you, how the industry might respond to the challenges in integrating and introducing the technology into today's system, what the doctor's visit of the future might really look like,
0:42 Hanne Tidnam? and ultimately, how AI can make healthcare more human.
0:46 Vijay Pande? Before we talk about technology and we talk about all the things that are changing the world or making huge impacts, it's interesting to think like, what should a doctor be doing? And how do you see that?
0:55 Speaker 02 Yeah, that's really what I was pondering. And I really did this deep look into AI. I actually didn't expect it to be this back-to-the-future story.
1:05 Vijay Pande? Yeah.
1:05 Speaker 02 But in many ways, I think it turns out that as we go forward, particularly in a longer-term view, the ability to outsource so many things with help from AI and machines, I think is going to get us back, it could, it could, that's the big if, to where we were back in the 70s and before.
1:24 Vijay Pande? What was better then was that doctors were spending much more time with us, right?
1:27 Speaker 02 Exactly, that gift of time, the human side, which is the center of medicine, that's been lost. The big business of healthcare and all of its components, like electronic records and relative value units and all this stuff, basically has sucked out any sense of intimacy and time. And it's also accompanied by lots of errors. But of course, it's not a gimme because administrators want more efficiency, more productivity.
1:57 Vijay Pande? Well, you know, I think you put this on Twitter where it's like, some kid drew like a drawing of going to the doctor and the picture was the doctor with their back turned, working on a computer. And, you know, that is what happens too much. But then, but yet we're talking about technology coming in. So how does this all work out that more technology means less computer?
2:15 Speaker 02 Well, I think that is a kind of fundamental of The problem of, you know, doctors not even making eye contact and as a child to draw that picture, well, how unnerving that was, her trip to the pediatrician, natural language processing can actually liberate from keyboards. And so it's already being done in some clinics and even in the UK in emergency rooms. And so if we keep that up and build on that, we can eliminate that whole distraction. Doctors and nurses and clinicians being data clerks, I mean, this is ridiculous. So the fact that voice recognition is just moving so fast in terms of accuracy and speed is really encouraging.
2:59 Vijay Pande? Alexa is a very basic version of voice recognition, but you're talking about something much more sophisticated now, something where they're actually doing NLP, they're doing transcriptions, the doctors don't have to take notes. If you were more sophisticated, you could put an ontology onto this such that you're not just getting like a transcript of what's going on, but that you have very machine learning friendly organized data?
3:18 Speaker 02 Exactly. So the notes that are synthesized from the conversation are far better than the notes that you would get in Epic or Cerner where 80% are cut and pasted and they're error laden. So, I mean, just as we Google AI published in JAMA of their experience, I think it's really going much faster because the accuracy of the transcription and the synthesized note is far better than what we have today and it exceeds professional medical transcription this in terms of accuracy.
3:51 Vijay Pande? Yeah. And so I'm imagining like what the doctor visit is like then. So we've got maybe NLPs, so the doctor doesn't have to be transcribing and not interacting with Epic. Who knows what's in the backend, but doesn't even matter anymore.
4:02 Speaker 02 Right.
4:03 Vijay Pande? Presumably you've got like billing and all that headache for, you know, like a PCP. All that's a huge headache.
4:09 Speaker 02 Oh, well, that's all part of that conversation because you say, well, you know, Mr. Jones, we're going to have you have the lab test for such and such, and then we're going to, you know, get this scan and it's all done through the conversation.
4:20 Vijay Pande? We could bring in other technologies, right? We've thought about, you know, just how imaging or other type of diagnosis comes in and it's, we've seen all these cool things about how. can improve this, but then also that it's funny because at the same point, and you bring this up in the book that over diagnosing also can be very difficult. Like it was very stunning where you talked about how the incidence of thyroid cancer is going up, but the mortality has been flat.
4:46 Speaker 02 Exactly.
4:47 Vijay Pande? And so I guess the challenge will be is how can we bring in these technologies in? So doctors can do the things they should be doing and not the things they shouldn't be doing.
4:56 Speaker 02 Right. Well, I think there is getting arms around a person's data. This whole deep phenotyping. So no human could actually integrate all this data. Not only the whole electronic record, which all too often is incomplete. But also, you know, pulling together sensor data, genomic data, gut microbiome. All the things that you'd want to be able to come up with not only better diagnoses, but also the better strategy for prevention or treatment. So I think what's going to make life easier for both doctors and for the patients is having that data fully processed, you know, and distilled.
5:39 Speaker 02 In the book, I tell the story about my knee replacement and how it was a total fiasco. Part of that was because my orthopedist who did the surgery wasn't in touch with my congenital condition. And so that hopefully is is going to be something we can transcend.
5:56 Vijay Pande? Yeah.
5:56 Speaker 02 In the future.
5:56 Vijay Pande? Well, and this is one thing that computers do can do very well as logistics and coordination. There's tons of cases where you might like thyroid cancer, we're just talking about, maybe you have to bring in an endocrinologist in addition to oncologist. And it's shocking that often there's no discussion there. There's no communication. But yet the challenge in my mind is how does computer magically know things that that we can't do right now?
6:19 Speaker 02 Yeah, well, that's, I think, where the complementarity, the synergy between machines and people is so ideal because we just have early satiety with data. Whereas deep learning has insatiable appetite. And so that contrast, but we have as doctors and humans, we have just great contextual abilities. The judgment, the wisdom experience and just the the the features that can basically build on that machine processing because we don't ever want to trust. An algorithm for a serious matter. But if it tees it up and we have oversight and we fit it into that person's story,
7:04 Speaker 02 that I think is the best of both worlds.
7:06 Vijay Pande? Well, here I'm really curious because what is the ground truth? Because generally, we don't trust an individual person as like as knowing everything either, right? So the the the ground truth would be like a a second opinion or a third opinion or a fourth opinion or even like, you know, a board to look at something. And that would be what I think most people would view as the ground truth.
7:24 Speaker 02 The ground truth, when it's applied to training an algorithm, of course, is knowing that it is the real deal, that it is really true. And I think a great example of that is in radiology. Because radiologists have a false negative rate of 32%.
7:42 Vijay Pande? Oh, yeah.
7:43 Speaker 02 False negative. And that's the basis for most of the litigation in radiology, which is, over the course of a career, a third of radiologists gets sued, mostly because they missed something. So what you have are algorithms with ground truths that are trained on, you know, hundreds of thousands of scans. So that whether it's a chest x ray, a CT scan or MRI, whatever it is, that it's not going to miss stuff. But then, of course, you've got that over read by the trained radiologists. So there, you know, I think that's an example of how we can use AI to really rev up accuracy.
8:19 Vijay Pande? Somebody is going to need to interpret the algorithms or sort of be on top in that, in a sense, the doctor is freed from the stuff the doctor shouldn't be doing, like the BS accounting or the typing or all these things. And that's not the best use of doctor's time. And it's funny because it seems like the best use of doctor's time is in understanding what these tests would mean, whether it be an AI test or cholesterol or whatever, and in how to communicate with the patient.
8:44 Speaker 02 Yes.
8:45 Vijay Pande? And so that seems to be a theme running through the book. So for the first half, in terms of the understanding, what do you think that's going to look like? I mean, one of the things I I've been constantly wondering about is whether there'll be a new medical specialty. Like, you know, because you don't have radiology without like CT or x rays, right?
9:00 Speaker 02 Right.
9:00 Vijay Pande? Zoom, you don't have radiology 100 years ago. Do you have AIology or something like that?
9:05 Speaker 02 Sarab Ja, who is a radiologist at Penn, he and I penned a JAMA editorial about the information specialist. Radiologists and pathologists, their foundation is reviewing patterns and information.
9:21 Speaker 02 But what's interesting is this is an opportunity for them to connect with patients. Because they don't, you know, right now.
9:28 Vijay Pande? Oh, yeah, pathologists never see some patients, right?
9:30 Speaker 02 Radiologists live in the basement, the pathologists look at the slides, that group of pathologists. But they actually want to interact with patients and they have this unique insight because they're like the honest brokers. They're not, they don't want to do a surgery. They want to give you their expertise. And so I think that's where we're going to see a pretty substantial change. And as you touched on this new specialty, it'll look different than the way it is today.
9:54 Vijay Pande? In that case, it almost seems like everybody is better off. The doctor's better off because the pathologist is not just looking at slides, but actually is dealing with patients. Presumably the patient's better off because you have these false negatives get found.
10:08 Speaker 02 The pathologists, you know, they have remarkable discordance when they look at slides. And to be able to have that basically looked at as if hundreds of thousands of them were reviewed. Getting back to your second, third and nth opinion, to get that as input for them to help and consult with a patient, I think is really a bonus.
10:27 Vijay Pande? We're talking about radiology here and pathology, but I mean, we could sort of think about this as an issue for diagnosis in general. And there's one thing you point in the book, I think really beautifully, you know, you said, once trained, doctors are pretty much wedged into their level of diagnostic performance throughout their career. And that's kind of an amazing thing is that doctors, I guess, go through CPE and so on, but like, you only go through med school once and that's an intense process, you learn a lot, but you can't go through med school all the time.
10:52 Speaker 02 No, it's so true. And that gets me to, you know, Danny Kahneman's book about thinking fast and slow. And the system one, that is the reflexive thinking that happens automatically. Versus what we want are reflective thinking, which is system two, which takes time. And it turns out that if a doctor doesn't think of the diagnosis of a patient in the first five minutes, is over 70% error rate. And that's how, actually, that's how much time is the average with patients. So we have the problem, as you've alluded to, of kind of a plateauing early on in a career. But we also suffer because of lack of time with the system one thinking.
11:35 Vijay Pande? The thought is that the machine learning is reflecting what system two would look like because it's trained from doctors sort of doing system two.
11:44 Speaker 02 Exactly. You know, it brings in the integration of what would be the ground truths of thousands for that particular data set. So I think it has a potential. And of course, a lot of this stuff needs validation, but there's a lot of promissory studies to date that suggest that's going to be very possible.
12:06 Vijay Pande? When I think about what ML or artificial intelligence AI could do, there's like two axes. There's ones like just scale, like the fact that you can scale up servers on Amazon trivially, much more than you could scale up human beings. Like we could scale up 1,000 servers right now or 10,000 servers right now. I don't, I don't think we could call 10,000 doctors and get them here. The other thing you could do, you know, is that you could sort of talk about other axes is like sort of intelligence or capability. And you're talking about both in a sense that you can scale up, not just the fact that you could have like a resident or med school student sort of doing things and having a lot of them. But you, in addition to that, you have, in a sense, a doctor through AI that's in diagnostics that's better than any single doctor.
12:49 Speaker 02 Right. I think that's really what is going to be one of the big early changes in this new AI medical era is that diagnosis is going to get so much better. Right now, we have over 12 million serious errors a year in the United States. And they're not just costly, but they hurt people.
13:08 Vijay Pande? Yes.
13:09 Speaker 02 So this is a real opportunity to upgrade that. And that's a much more of a significant problem than most people realize.
13:15 Vijay Pande? Well, and so, so far, we've been talking about things that feel like the sci-fi fantasy version of stuff, right? I mean, like, because we've got like this doctor of sorts through diagnosis that can do what no single doctor could do, presumably at scale, it's doing this at lower cost. It's allowing human beings to do the things they should be doing. Is there any dystopian turn here or how does this?
13:37 Speaker 02 No shortage of those.
13:39 Vijay Pande? And so how could, how could this go wrong and what can we do to prevent it?
13:42 Speaker 02 Well, I mean, I think. One of the things we've harped on is that you've got to have human oversight. We can't trust an algorithm absolutely because for any serious matter. Because if we do that and it has a glitch or it's been hacked or it has some kind of adversarial input, it could hurt people at scale. So that's one of the things that we got to keep an eye on. For example, if an algorithm gets approved by the FDA, oftentimes it's, these days, it's, it's an in silico retrospective sort of thing.
14:13 Vijay Pande? Yep.
14:14 Speaker 02 And if we just trust that without seeing how it performs in a particular venue, a particular cohort of people, these are things that we just shouldn't accept blindly. So there's lots of deep liabilities and it runs from, of course, privacy, security, the ethics, many aspects that are not ideal about this. But when you think about the need and how much it could provide to help medicine, I think those are the trade offs that we have to really consider.
14:42 Vijay Pande? What we should be doing now and what people could be doing now to sort of anticipate this? Or do you think it's like too early? I mean, because people have these algorithms now. And what do we see in one year versus five years versus 10 years?
14:54 Speaker 02 Well, it is rolling out in other parts of the world. You know, I just finished this review with the NHS and that was fascinating because they are really going after this. They are the leading in the world force in genomics and now they want to be in AI. So they already have emergency rooms that are using, that are liberated from keyboards and they are going after this. And this, of course, in the middle of Brexit. So that's kind of amazing. But China is really implementing this. You could say, well, maybe too fast because out of desperation or need. But one of the advantages that we don't recognize with China, not just that they have scale in terms of people, but they have all the data for each person.
15:38 Vijay Pande? Yeah, that's right.
15:38 Speaker 02 And we have no data for each person. Basically, our data is just spread around all these different doctors and health systems. Nobody has all their data. And that is a big problem because without the inputs. Better complete.
15:52 Vijay Pande? For like you personally.
15:53 Speaker 02 Yeah, yeah, then what are you going to get out of that? So this is a this, we are at a handicap position in this country. And the other thing, of course, is we have no strategy as a nation. Whereas China, UK, and many other countries, they are developing or have developed planning and strategy and put in resources. Here, as a nation, we have zero resources. In fact, we have proposed cuts to the same, you know, granting agencies that would would potentially help.
16:20 Vijay Pande? Yeah, so what what should one do at that scale? Like, you know, there's various things people propose, you know, is this a something to have a new national institute of health, you know, in this area? Is there, I mean, I think when I think about government playing a role, I think I want them to try to help them build the marketplace and and set the rules. But we have to be careful that we don't put too much regulation as well. I mean, what are what do you I mean, when you say we don't we don't have a strategy, what what's missing? What should we be doing?
16:47 Speaker 02 Yeah, well, we have no national planning or strategies. How is AI not only for healthcare, but in general? How is it going to be cultivated and made transformative? The experience I had in the UK was really interesting because there they not only have the will, but they have a a whole wing of the NHS for education and training. You just think about it. We already talked about professions within medicine that are going to have a morph of their daily function. So we're not well prepared.
17:23 Vijay Pande? Who should take the lead then?
17:25 Speaker 02 One of the problems we have that you're touching on is our professional organizations haven't really been so forward thinking. They mainly are centered on maintaining reimbursement for their constituents. The, you know, entities like NIH and NSF and others could certainly be part of the solution. What you want to do here, I think, is to really accelerate this. We're in the middle of an economic crisis in healthcare, which is in the US, the worst outlier. I mean, we're spending over $11,000 per person and we have the worst outcomes. Life expectancy going down three years in a row.
18:02 Vijay Pande? Yeah.
18:03 Speaker 02 Childhood mortality, infant mortality, maternal mortality, the worst. People don't realize that. Then you have the UK and so many other countries that are at the $4,000 per year level. And they have outcomes that are far superior. So if we use this, we could actually reduce inequities. We could make for a far better business model, paradoxically, but we're not grabbing the opportunity.
18:28 Vijay Pande? Maybe there's another solution we could think about, which you also point to in the book, which is what if, what can we do to drive through consumer action? For instance, a lot of our healthcare is sick care, right? What happens when you get sick?
18:40 Speaker 02 That's almost all of it.
18:41 Vijay Pande? What about, what can we do to stay healthy? First thing I think of is diet and lifestyle, right? That could go a long way in so many diseases, how many things that we deal with. So actually you touch on diet. Before we even talking about like diagnosing whether you have cancer, should we be diagnosing what should you be having for lunch?
18:57 Speaker 02 Yeah, I couldn't agree more that that should be a direction. We have had this so naive notion that everyone should have the same diet. And we never got that right as a country. But now we know without any question that people have an individualized and highly heterogeneous response. That's not just through glucose spikes. If you and I ate the exact same food, the exact same amount, the exact same time, our glucose response would be very different. But also triglyceride response would be different and they don't track together. So what we're learning is if you get all this multimodal data, not just your gut microbiome and sensor data and your sleep and your activity,
19:40 Speaker 02 your stress level and what exactly you eat and drink. We can figure out what would be promoting your health. We're not there yet, but we're seeing some pretty rapid progress.
19:50 Vijay Pande? What's intriguing to me is that in cases, there are cases now, especially let's say just glucose, where you can take technology developed for type one or type two diabetics. And now, I'm not diabetic, but like I actually had the sort of, sort of, I was about to say joy, but at least the intellectual intrigue of having a CGM on me for two weeks.
20:09 Speaker 02 Yeah.
20:09 Vijay Pande? And so I got to play all these experiments.
20:11 Speaker 02 Right.
20:11 Vijay Pande? Right? Like, so I tried white rice or brown rice.
20:13 Speaker 02 Yeah.
20:14 Vijay Pande? How's ice cream? You know, wine versus scotch, you know, all the important questions one has to figure out.
20:19 Speaker 02 Exactly.
20:20 Vijay Pande? And actually, it was actually a surprise to me that how, for instance, I did not spike on ice cream. Spiked on brown rice.
20:28 Speaker 02 Yeah.
20:29 Vijay Pande? I'm not sure I'm prepared to go on the ice cream diet just yet. And I don't think you would prescribe that either, right? But I think the idea is that it's just different for everybody, right? So maybe you spike on ice cream, I don't. And that, you know, what's I think been kind of so annoying about nutrition is that we hear all these conflicting things. But perhaps part of the reason why we're hearing these conflicting things is that it is so individual.
20:50 Speaker 02 Exactly.
20:51 Vijay Pande? And that it's so complicated and such a fundamental data science problem that it probably takes something like machine learning to figure it out.
20:57 Speaker 02 Well, I think that's central. If we didn't have machine learning, we wouldn't have known this. And only, you know, thanks to the group at the Weizmann Institute in Israel, they cracked the case on this.
21:07 Vijay Pande? Aaron Segal's work?
21:08 Speaker 02 Yeah, Aaron Segal. And now it's been replicated by many others and it's being extended. But what would be promoting your health? And right now, it's, you know, these proxy metrics like your glucose or your lipids in the blood. But eventually, we'll see how outcomes and prevention can be fostered by your diet.
21:30 Vijay Pande? It's really kind of mind blowing how difficult a data science problem it seems nutrition is.
21:34 Speaker 02 Yeah, the problem, Vijay, is the number of levels and the sea of data. I mean, we're talking about terabytes of data to crack the case for each individual. So it's not even just your gut microbiome of the species of bacteria and their density. But now we know it's the sequence of those bacteria that are part of the story. Then you have, of course, your, these continuous glucose every five minutes for a couple of weeks. That's a lot of data. Besides that, you've got, you know, all your physical activity, your, your sensors for stress, you know, your sleep data.
22:13 Speaker 02 And then are your even your genomics. So when you add all this together, this is a real challenge. No human being could assimilate all this data. But what's interesting is not only at the individual level, but then with thousands of people. So you take everything we just talked about, multiply by thousand or hundreds of thousands. That's how we learn here. And so what I think is the biggest thing about the AI under appreciation is the things that we're going to learn that we didn't know. Like, for example, another great example is when you give a picture of a retina to international retina expert, and you say, is this from a man or a woman? The chance of them getting right is 50 50.
22:56 Speaker 02 But you can train an algorithm to be over 97, 98% accurate. And there's so many examples like that, like you wouldn't miss polyps in a colonoscopy, which is a big issue. Or you would be able to see your potassium through your smart watch level in your blood without any blood. And then the imagination just runs wild as far as what you could do when you train things. And so training your diet with this torrent of data, not just from you, but from a population is, I think, a realistic direction.
23:29 Vijay Pande? And what I think is interesting about this is that it's something where A, you know, we don't need the AMA or NIH or anything else to get involved in terms of diet. And B, actually, people want to take care of these problems because I think most people are motivated. We just don't know what to do.
23:45 Speaker 02 Right. And so many aspects of it, like, you know, now chronobiology is really this hot topic. That's about your circadian rhythm. And should you eat only for eight hours during the day? Well, certain people, yes. But you know, the whole idea that there's this thing for everyone, we got to get over that. That's what deep phenotyping is all about, to learn about the medical health essence of you. And we haven't had the tools until now to do that.
24:13 Vijay Pande? Okay, so there's a ton of data. But like, a lot of it seems kind of subjective, right? I mean, did I sleep well or not? How do you sort of overcome the fact that not everything is quantitative, like, you know, my cholesterol level?
24:25 Speaker 02 Well, as it turns out, that was kind of old medicine, where we just talked about your symptoms. But new medicine is with all sorts of objective metrics. So a great example of this is state of mind or mood. And that's going to be transformative for mental health. Because now, everything from how you type on your smartphone to the voice,
24:48 Vijay Pande? Yes.
24:48 Speaker 02 which is so rich in terms of tone, intonation, to your breathing pattern, to your facial recognition of yourself. I mean, there's all these ways to say, you know, Vijay, you're really depressed.
25:02 Vijay Pande? Yeah.
25:02 Speaker 02 You know that you're depressed. So the point being is that you have objective metrics of one's mental health. As a cardiologist, for all these years, I'd have these patients, they'd come and tell me, I feel my heart fluttering. And I would put in the note, the heart's fluttering. That was so unhelpful. Now I can say, well, you know, you should be able to record this on your phone, or if you have a smartwatch, and when your heart flutters, just send me the PDF of that. And we have the diagnosis. That is real world, no longer subjective, a whole different look, really. And by the way, the patient who has the fluttering, who records their cardiogram, they don't have to wait for me.
25:47 Speaker 02 They already have an automated read from AI that's more accurate than a doctor.
25:52 Vijay Pande? There's something very anecdotal about the doctor visit. It's not right there in the moment.
25:57 Speaker 02 Yeah.
25:57 Vijay Pande? Right?
25:57 Speaker 02 It's a one-off.
25:59 Vijay Pande? Yeah, it's a one-off. And so it's a funny thing, because people wonder about, let's say, the knock on a wearable will be that it's not like an 8-point EKG or something like that. But on the other hand, it's there with you all the time.
26:10 Speaker 02 Yeah, no, exactly. And then there's this contrived aspect of going to see the doctor, where a lot of people find that very stressful. And when we talk about white coat hypertension, we don't even know what normal blood pressure is. Because we need to check that out in thousands, hundreds of thousands of people in their real world to find out what's normal. We've already had this chaos of the American Heart Association saying that they changed the blood pressure guidelines on the basis of no data. Speaking of lack of objective metric.
26:42 Vijay Pande? Yeah. Well, so one other area that I thought was really intriguing, and to me, this was almost paradoxical, the concept of AI being useful for empathy. Because I would have thought like if we're thinking about the things that a computer is good at, like multiplying numbers, that's going to be something like they're going to beat humans at that any day. I would have thought though that empathy would be the one, like the last bastion of what we're good at.
27:04 Speaker 02 Yeah.
27:05 Vijay Pande? And what the computer is good at. But how does AI get the empathy? Because as we started the conversation with us about how that's a key part of what a doctor does, well, like what can AI do there?
27:14 Speaker 02 Well, we are missing that in a big way today. And how do we get it back? Well, you know, I think what how we get it back is we take this deep phenotyping, we do deep learning about the person, and that's all outsourced with oversight, you know, for a doctor or clinician. Now, when you have this remarkable improvement in productivity, in workflow, and efficiency, and accuracy, all of a sudden, you have the gift of time. If we just lay down as we as doctors have over decades for administrators to go ahead
27:54 Speaker 02 and just drive revenue, and basically have no consideration for patients or doctors, we're not going to see any growth of empathy. We're not going to see the restoration of care in healthcare. But if we stand up and if we say that time, all that, all that benefit of the AI part, the machine support, and by the way, that's also at the patient level. So the patients now with their algorithmic support, they're decompressing the doctor load too.
28:23 Vijay Pande? Yeah, yeah, they're doing some of it for the doctor.
28:25 Speaker 02 A lot of simple things, ear infections, skin rashes, and all that sort of stuff that's not life threatening or serious, but it's bypassing a doctor potentially almost completely. So between this fly, flywheel of algorithmic performance enhancement, if we stand up for patients, then we have all this time to give back. Once we have time to give back, then we tap into why did humans go into the medical profession in the first place? And the reason was because they want to care for their fellow human being. But they lost their way. And now we have the peak burnout and depression and suicide in the history of the medical profession. And by the way, not just in the US, you know, in many parts of the world. And how are we going to get that back?
29:09 Speaker 02 Because it turns out, if you have a burnout doctor, you have a doubling of errors. And it's a vicious cycle. You have errors.
29:17 Vijay Pande? And they get more depressed.
29:19 Speaker 02 More depressed. So we have to break that up. And I think if we can get people so there's time together and that real reason why the mission of healthcare is brought back, we can do this. It's going to take a lot of activism. It's not going to be easy and it's going to take a while. But if we don't start planning for this now, it's not going to happen.
29:41 Vijay Pande? Yeah. How do you think that changes for, you know, what, how do you become a doctor? I mean, getting into med school and all the training is really difficult. What does the future of medical education look like? Like, right now, pre-med degree is a lot of like biology and chemistry, not too much effort in psychology or empathy or in statistics or in machine learning. You know, what does that look like in the future?
30:07 Speaker 02 I think we're missing the mark there. We continue to cultivate brainiacs who have the highest MCAT scores and grade point averages and oftentimes relatively low on emotional intelligence. So we have tilted things. We want to go the other way. We want to emphasize who are the people who have the highest interpersonal skills, communicative abilities, and who really are the natural empathetic people. Because a lot of that brainiac work is going to be machine generated. And so, you know, it's something that we all start to lean in that direction.
30:45 Vijay Pande? Yeah. Now, and it's intriguing because I think there's a chicken and egg problem here because I think first this has to be put in. You know, often in these, any of these big changes, there will be resistance. Who's going to be fighting this?
30:56 Speaker 02 The resistance, we have to anticipate is going to be profound. One of the problems is that the medical profession, it may not be ossified, but it's very difficult to change. When it, the only changes that ever occurred rapidly, like adoption of robotics in surgery, were because it enhanced revenue. That we, these are, none of these things are going to enhance revenue. They're actually going to potentially be a hit. We have all these interests that this is going to challenge. Like, for example, we could get remote monitoring. Of everyone in their home, instead of being in a hospital room unless they were needing an intensive care unit. Now, do you think hospitals are going to allow that to happen?
31:41 Speaker 02 Because they would be gutted. And then they won't know what to do with all their facilities. So the American Hospital Association is not going to like this.
31:50 Vijay Pande? Well, so I'll be delus of it here. They're not going to like it revenue wise. Would they say that would put patients in danger because they don't, obviously, you don't, at home, you don't have what a hospital has?
31:59 Speaker 02 Well, you know what's interesting thing is, I don't know if you get in more danger than going into our hospitals. One in four people are harmed.
32:05 Vijay Pande? Yeah, you mean sepsis and infection?
32:07 Speaker 02 The main thing, nosocomial infections from the hospital. Yeah, but other medication errors and other things. The comfort of your own home. You can actually sleep, you be with your loved ones, the convenience. But most importantly, just think of the difference in expense. You could buy years of broadband data plan for one night in the hospital, which is $5,000 on average. It's amazing. We have the tools to do that now, but you're not seeing it being seriously undertaken because of the conflicts.
32:38 Vijay Pande? You know, so if you think about how all this has to actually happen, you know, we talked about what's possible, but like if you get to nuts and bolts, it's interesting to think who's going to do it. Because if you take just a pure data scientist who doesn't understand the medicine, I don't know if that would be enough, right? And, but also, I don't know if you could take a doctor that doesn't understand data science, right? And so, is it going to be teams of commingled groups that get this together? Because there will be iterations between the data science and the biology and the clinical aspects that have to come, have to come one after the other to be able to make these advances?
33:14 Speaker 02 We need machines and people to get the best of both worlds. So in the book, that example of how we cracked the potassium case between Mayo Clinic cardiologists and a live core data scientist. And what was amazing about that experience to review with them was that the cardiologist thought you should only look at one part of the cardiogram, which is historically known as so-called QT interval, because it was known to have something to do with potassium. But When that flunked and the algorithm was a farce, the data scientist said, well, why are you so biased? Why don't we just look at the entire cartogram? And by the way, Mayo, you only gave us a few million cartograms.
33:58 Speaker 02 And why don't you give us all the cartograms? And so then they nailed it. So the whole idea is that the biases that we have that are profound. But when you start de-biasing both the data scientists and the doctors, the medical people, then you start to get a really great result.
34:18 Vijay Pande? One of the scary stories I saw was that this algorithm was getting cancer, no cancer, right with crazy high accuracy, like AUC of like 1.0, like never making a mistake. And it turned out that there was some subtle difference between like a high Tesla magnet and a low Tesla magnet. And that the patients who were very sick to start off with were always getting one type of scan. And that a human being couldn't tell the difference, but that the machine was picking up some signal, not of whether it was cancer, no cancer, but whether they were getting like the fancy measurement or the less complicated one. Or another great example is like there's a classic example where they're, I think predicting tumors and they had rulers for the size of the tumor on all the tumor ones. And so really, ML was a great ruler detector.
35:03 Speaker 02 Yeah, the whole idea that as a pathologist, we can't see in a slide the driver mutation.
35:09 Vijay Pande? Oh, of course.
35:09 Speaker 02 But you could actually train the algorithms. So that when the pathologist is looking at it, it's already giving you what is the most likely driver mutation. It's incredible. And that does get me to touch on the deep science side of this, which we are recognizing is way ahead of the medical side. The ability to upend the microscope. You don't have to use fluorescence or H and E. You just train. So you forget staining. The idea that you used to be hard to find rare cells, just train the algorithm to find the rare cells. I mean, we're seeing some things in science, no less in drug discovery, in processing cancer and sequencing data, and certainly in neuroscience.
35:53 Speaker 02 It's a real quiet. Revolution that's much further ahead than on the medical side because there's no regulatory hurdles.
36:00 Vijay Pande? And you make a good point because I think it's tempting to just try to do what the human can do better. Or what the human can do better now try to do as well. But now you're talking about doing things that no human being could do.
36:11 Speaker 02 Yeah.
36:11 Vijay Pande? Imaging plus genomics where the genomics readout, let's say, or the whatever the the blood assay is the gold standard. I don't want to predict what the pathologist would say. I want to predict the biopsy or I want to predict the the blood or whatever the the true gold standard is behind it.
36:26 Speaker 02 Right.
36:26 Vijay Pande? And if you're training on the best labels, you can do things that no human being could do.
36:30 Speaker 02 Well, you know, this may be the most important point is that we have to start having imagination. Because we don't even have any idea of the of the limitless things that we could teach machines. Because I'm getting stunned almost on a weekly basis. I said, I never would have thought of that. And so just fast forward, here we are in 2019. What's it going to be like? You know, a few years of all the things like when the Mayo Clinic told me they could look at a 12 lead cardiogram for millions. And be able to say this person's going to get Achophypalization in their life with X percent probability. I said, really? And they've done it. And so I I never would have expected that.
37:10 Vijay Pande? Yeah, well, and that's a really fun point because and you could either think of two ways. That the human beings aren't being imaginative or or or what does imagination mean for an algorithm?
37:21 Speaker 02 Right, well, if we get into heavy into unsupervised learning. We're a bit limited by the the annotation and the and the ground truth.
37:30 Vijay Pande? Yes.
37:30 Speaker 02 Going back to that, you can only imagine things when you have those for supervised learning. But, you know, as we go forward, we'll have more of those data sets to work with. And we'll we'll be better at going forward without with well with federated data sets and and unsupervised learning. So the the opportunities going forward are are pretty enthralling.
37:50 Vijay Pande? Yeah, well, the unsupervised learning is interesting because you can finally just. You know, and for those aren't familiar with the term, it's kind of like trying to find the clusters to sort of not have the labels, but to see the lay of the land. And that's interesting because no human being can sort of, especially in a high dimensional space like visualize. And see that. And so that's one thing. But the second thing is that if you just throw all of the data in and maybe have the algorithm make sure that it's not overfitting, that it's not trying to find an overly complicated story, almost like, you know, these conspiracy theories are like human beings overfitting for for the moon landing being a hoax or something like that, when there's a simpler explanation for things. If you keep it to a simple explanation, the computer can try everything. Yeah, so like you talked about, it could look at the whole cardiogram. It could look at things that
38:35 Vijay Pande? we don't look at because either we're expert enough to know that couldn't possibly right, even if it is. Or we just don't have the time. It reminds me sometimes these algorithms almost like children in that kids just don't know until they'll try things. And that's where imagination and creativity often comes from.
38:53 Speaker 02 I couldn't agree with you more.
38:55 Vijay Pande? So we've been spending a lot of time talking about diagnosis, but prediction is another thing that is really important.
39:00 Speaker 02 I'd call that a real soft spot in AI. And I show I I told the story of my father-in-law, who kind of was my adopted father, because in the book, about how he was on death's door. He was about to come to our house to die. And he was resurrected. But any algorithm would have said he was a goner. And so the idea that at the individual level, you could predict accurately, whether it's end of life, or when you're going to die, or in the hospital, this is how long you're going to stay, or you're going to be readmitted, all these things. We're not so good at that. We we can have a general sense from a population level.
39:42 Speaker 02 But so far, prediction hasn't really panned out nearly as well as classification, diagnosis, triage, that kind of stuff. And I I still think that that's one of the shakier parts, because then you're you're going to tell a person about a prediction. You know, we're not very good at that. When we talk to people with cancer and we tell them, you know, their their prognosis, it's all over the place in reality. And so, you know, the the question is, are algorithms algorithms really going to do better? Or are they just going to give us a little more precision, maybe not much?
40:16 Vijay Pande? And is there enough information to ever predict anything like that?
40:21 Speaker 02 Well, that's a part of the problem, too, is that the studies that have been done to date, things like predicting Alzheimer's, predicting all sorts of outcomes you can imagine, they're not with complete data. They're just taking what you can get, like what's in one electronic health record, one system, rather than everything about that person. So maybe it will get better when we fill in the holes.
40:43 Vijay Pande? I always think about what would be the interesting challenges to work on. That's like one of the most interesting ones.
40:48 Speaker 02 I think it is because there you could improve the efficiency. If you knew who are the people at the highest risk and who you want to change in natural history, what their algorithm is predicting, if it's something that's an adverse outcome. So that eventually we'll probably get there, but it isn't nearly as refined as the other areas.
41:06 Vijay Pande? But if you combine all these things together, you know, this thing where you're monitoring your body every five minutes and your diet and your exercise and your drugs and you have all this longitudinal data, that's something that no one's ever had before.
41:19 Speaker 02 Yeah, well, you're bringing up the a big hole in the story, which is multimodal data processing. We are not doing it yet. You know, like a perfect example is like in diabetes, people have a glucose sensor and the only algorithm they have tells them whether glucose is going up or down. That's pretty dumb. Why isn't it factoring in everything they eat and drink and their sleep and activity and the whole works? Someday we'll have multimodal algorithms, but we're not there yet.
41:51 Vijay Pande? Well, so let's go back to where we start, you know, a visit to the doctor in the future and like the good news is that the doctor doesn't have to do any of the typing or recording, AI is sort of figuring out the diagnosis and that the doctor has all the time now to actually be empathetic and communicate, which is great. But is that now all that's left?
42:10 Speaker 02 No, no, not at all because human touch. So when you go to see a doctor, you want to be touched. That's the exam part of this. People, when they get examined for their heart and you don't even take off their shirt, they know, they know there's a shortcut going on here.
42:30 Vijay Pande? Yeah, that's interesting.
42:31 Speaker 02 They want to have a thorough exam because they, they know that that's part of the real experience.
42:41 Speaker 02 And so what we're talking about is the exam may change. Like, you know, for example, I don't use a stethoscope. I use a smartphone ultrasound and do an echocardiogram. And I show it to the patient together as we're doing it in real time, which the person would never see. And by the way, they wouldn't know what lub dub looks like, which you sure can see or sounds like, but you sure can show them.
43:01 Vijay Pande? Yeah.
43:02 Speaker 02 So the tools of the physical exam may change, but the actual hands-on aspects of it and the interaction with the person, the patient, and that that's the intimacy. And we've lost that, too. You know, the physical exams have really gotten very much a a distraction from what they used to be. I mean, we need to get back to that. That's what people want when they go see a doctor.
43:28 Vijay Pande? Yeah, the people have deprecated exams because essentially they said they weren't of value. But it sounds like what was being done was not the part that needed to be done.
43:36 Speaker 02 When you're dealing with analog tools and, you know, they can be so superseded by the things we have today. And when you're sharing them with the patient, so here's what you have. And then you send them the video files or the metrics so they can look at, you know, when they get home and get more familiar with their body. It's not only the physical exam that happens instantaneously in the encounter, but the ability to have that archived data that people get more, they learned about themselves. That's all part of that awareness that's important.
44:08 Vijay Pande? And you know, you talked about Back to the Future. There might be another sci-fi analogy. I think there's some Star Trek episodes like this where actually the group that has the highest technology is the one where the technology is invisible.
44:18 Speaker 02 Yeah.
44:19 Vijay Pande? And it sounds like that's what the, all of this is going to be in the background.
44:22 Speaker 02 That's right.
44:23 Vijay Pande? But but that you really are interacting with a person. And this person now has just these powers that they couldn't have before.
44:29 Speaker 02 Yeah, I'm with you all the way.
44:31 Vijay Pande? Well, thank you so much. This has been fantastic.
44:33 Speaker 02 I really enjoyed it.
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