#309 ‒ AI in medicine: its potential to revolutionize disease prediction, diagnosis, and outcomes, causes for concern in medicine and beyond, and more | Isaac Kohane, M.D., Ph.D.

29:57With very soon consequences in medicine, it was only six years later that we saw journal articles about recognition of retinopathy, diseases affecting the retina in the back of your eye in diabetes and a paper coming out of all places from Google saying we can recognize different stages of retinopathy based on the images of the back of the eye. And that also was a wake up call because yes, part of the goal post moving is great that we could recognize cats and dogs in web pages. But now all of a sudden, this thing that we thought was specialized human expertise could be done by that same stack of software, just if you gave it enough cases of these red and apathies, it would actually work well.

30:47And furthermore, what was wild was that there's something called transfer learning where you tune up these networks, get them to recognize cats and dogs. And in the process of recognizing cats and dogs, it learns how to recognize the rules. Circles and lines and fuzziness and so on. You did a lot better in training up in your own network first on the entire set of images and then on the retinas and if you just went straight to, I'm just going to train on the retinas. And so that transfer of learning was impressive and then the other thing as a doctor was impressive to many of us. I was actually asked to write an editorial for the Journal of the American Medical Association in 2018 when at Google article was written that was impressive to us.

31:34was that what was the main role of doctors in that publication? It was just two fold. One was to just label the images that we're used for training. This is Ratnopathy is not Ratnopathy and then to serve as judges of its performance. And that was it. The rest of it was computer scientists working with GPUs and images tuning it and that was it. Didn't look anything like medical school and you're having expert level recognition of renopathy. That was a wake up call. You've alluded to the 2017 paper by Google attention is all that is needed. I think is the title of the paper. Attention is all you need.

32:19That's not what I'm referring to. I'm also referring to that. 2018 paper in JAMA. I'm sorry. You're talking about the great paper. Attention is all you need. That was about the invention of the transformer, which is a specific type of neural network architecture. I was talking about these were vanilla, fairly vanilla convolutional neural networks, the same one that can detect dogs and cats. It was a big medical application written up at the 2018. Except for computer scientists, no one noticed the attention is all you need paper. And Google had this wonderful paper that said, You know, if we recognize not just text that collates Cates together, because previously, so we're going to get away from images for a second.

33:06There was this notion that I can recognize a lot of similarities in text. If I see which words occur together, I can implicate the meaning of a word by the company it keeps. and so if I see this word and it has around it, kingdom, crown, throne, it's about a king, and similarly for Queen and so on. That kind of association in which we created what was called embedding vectors, which just in plain English it's a string of numbers that says, for any given word, what's the probability how often do these other words co -occur with it? And just using those embeddings, those vectors, those lists of numbers that describe the co -occurrence of other words, we were able to do a lot of what's called natural language processing.

34:03We're looking at text and saying, this is what it means, this is what's going on.

34:14But was the insight that where exactly the thing that you're focusing on was in the sentence, what was before and after the actual ordering of it mattered, not just the simple co -occurrence, that knowing what position that word was in the sentence actually made a difference, that paper showed the performance went way up in terms of recognition. And that transformer architecture came from that paper made it clear for number of researchers, not me, that if you scaled that transformer architecture up to a larger model so that the position dependence and this vector was learned across many, many more texts, the whole internet.

35:04You could train it to do various tasks. This transformer model, which is called the pre -trained model, So, I apologize, I find it very boring to talk about because unless I'm working with fellow nerds, this transformer, this pre -trained model, can think of it as the equivalent of an equation with multiple variables. In the case of GPT -4, we think it's about a trillion variables. It's like an equation where you have a number in front of each variable, a coefficient, that's about a trillion long. and this model can be used for various purposes. One is the chatbot purpose, which is given this sequence of words, what is the next word that's going to be said?

35:47Now, that's not the only thing you could use this model for, but that turns out to have been the breakthrough application of the Transformer model for text. Just to round out what you said earlier, Zach, would you say that is the third thing that enabled this third wave of AI the transformer? It was not what I was thinking about. For me, I was thinking of the real breakthrough in data -driven AI. I put around the 2012 era. This is yet another, if you talk to me in 2018, I would have already told you in a new heyday and everybody would agree with you. There was a lot of excitement about AI just because of the image recognition capabilities.

36:27This was an additional capability that's beyond what many of us were expecting just from the scale -up of the neural network. The three, just to make sure I'm consistent, was large data sets, multi -level neural networks, aka deep neural networks, and the GPU infrastructure. That brought us well through the 2012 to 2018. The 2017 blip that became what we now know to be this whole large language model transformer architecture, that development. I'm anticipated for many of us, but that was already on the heels of a ascendant AI era. There was already billions of dollars of frothy investment in frothy companies, some of which did well and many of which did not do so well.

37:19The transformer architecture has revolutionized many parts of the human condition, I think, but it was already part of it. I think the third wave. There's something about GPT where I feel like most people by the time GPT -3 came out or certainly by 3 .5. This was now outside of the purview of computer scientists, people in the industry who were investing in it. This was now becoming as much a verb as Google was in probably the early 2000s. There were clearly people who knew what Google was in 96 and 97, but by 2000 everybody knew what Google was, right? Something about GPT 3 .5 or 4 was kind of the tipping point where I don't think you cannot know what it is at this point.

38:12I don't know if that's relevant to the story, meaning does that sort of speak to what trajectory we're on now. The other thing that I think Zach has become so audible in the past year is the elevation in the discussion of how to regulate this thing, which seems like something you would only argue about if you felt that there were a chance for this thing to be harmful to us in some way that we do not yet perceive. So what can you say about that? Because that's obviously a nod to the technical evolution of AI that very serious people are having discussions about pausing moratoriums, regulations.

38:59There was no public discussion of that in the 80s, which may have spoke to the fact that in the 80s, it just wasn't powerful enough to pose a threat. So, can you maybe give us a sense of what people are debating now? What is this smart, sensible, reasonable argument on both sides of this. And let's just have you decide what the two sides are. I'm assuming one side says pedal to the metal. Let's go forth on development. Don't regulate this. Let's just go nuts. The other side is no, we need to have some breaks and barriers. Not quite that. So you're absolutely right that chat bots have now become a commonly used noun.

39:36and that probably happened with the emergence of GPT 3 .5 and that appeared around December of 2022. But now, yes, because out of the box that pre -trained model I told you about, I could tell you things like, how do I kill myself? How do I manufacture a toxin? It could allow you to do a lot of harmful things. So there was that level of concern. We can talk about what's been done about those first order efforts. Then there has been a group of scientists who interestingly went from saying we'll never actually get general intelligence from this particular architecture to saying, oh my gosh, this technology is able to inference in a way that I had not anticipated.

40:28And now I'm so worried that either because it's malevolent or just because it's trying to do something that has bad side effects for humanity, it presents an existential threat. Now on the other side, I don't believe is anybody saying let's just go heads down and let's see how fast we can get to artificial a shoulder general one tells us, or if they do think that, they're not saying it openly. Can you just define AGI, Zach? I think we've all heard the term, but is there a quasi -accepted definition? First of all, it's not, and I hate myself for even bringing it up because it starts... I was going to bring it up before you, anyway, it was inevitable.

41:08That was an unfortunate slip because artificial general intelligence means a lot of things to a lot of people. and I slipped because I think it's again a moving target and it's very much, I am the holder. There's a guy called Elias Yodkowski, one of the so -called doomers. And he comes up with great scenarios of how a sufficiently intelligent system could figure out how to persuade him things to do bad things or control of our infrastructure to bring down our communications infrastructure or airplanes out of the sky. and we can talk about whether that's relevant or not. And on the other side, we have, let's say, open AI in Google.

41:50But what was fascinating to me is that open the AI, which working with Microsoft generated GPT -4, we're not saying publicly at all. Let's not regulate it. In fact, they were saying, please regulate me. Sam Altman went on a world tour where he said, we should be very concerned about this. We should regulate AI. And he was before Congress saying we should regulate AI. And so I feel a bit churlish about saying this because Sam was kind enough to write forward to the book I wrote with Peter Lee and Kerry Goldberg on GPT -4 and the revolution in medicine. But I was wondering why were they insisting so much on regulation?

42:32And there's two interpretations. One is just a sincere and it could very well be that, sincere wish that it would be regulated. So we check these machines, these programs to make sure they don't actually do anything harmful. The other possibility unfortunately is something called regulatory lock -in, which means I'm a very well -funded company, and I'm going to create regulations with Congress about what is required, which boxes do you have to check in order to be allowed to run? If you're a small company, you're not going to have a bevy of lawyers with big checks to comply with all the regulatory requirements.

43:11And so I think Sam is, I don't know him personally, I imagine he's a very well motivated individual. But whether it's for the reason of regulatory lock -in or for genuine concern, there has not been any statements of, let's go heads down. They do say, let's be regulated. Now, having said that, before you even go with a doomer scenario, I think there is someone just as potentially evil that we have to worry about, another intelligence, and that's human beings. And how do human beings use these great tools? So just as we know, for a fact that one of the earliest users of GPT -4 were high schoolers trying to do their homework and solve hard puzzles, came to them, we also know that various parties have been using the amazing text generation and interactive capabilities of these programs to spread this information, to chatbots, and there's a variety of line things that could be done by third parties using these engines.

44:16And I think that's for me the clear and present danger today, which is how do individuals decide to use these general purpose programs. If you look at what's going on in the Ukraine Russian War, I see more and more autonomous vehicles flying and carrying weaponry and dropping bombs. And we see in our own military a lot more autonomous drones with greater and greater autonomous capabilities. Those are purpose -built to actually do dangerous things. And a lot of science -fiction fans will refer to SkyNet from the Terminator series, but we're literally building it right now. In the Terminator, Zach, they kind of refer to a moment.

45:09I don't remember the year like 1997 or something. And I think they talk about how SkyNet became, quote, self -aware. And somehow when it became self -aware, it just decided to destroy humans. Is self -aware movie speak for AGI? Like what do you think self -aware means in more technical terms? Or is it super intelligence? There's so many terms here and I don't know what they mean. Okay, so self -awareness means a process by which the intelligent entity can look back, look inwardly at its own processes and recognize itself. Now that's very hand -wavy but Douglas Hofster has probably done the most thoughtful and clear writing about what self -awareness means.

45:57I will not do it justice but if you really want to read a wonderful book that spends a whole book trying to explain it It's called I Am A Strange Loop. And in I Am A Strange Loop, he explains how if you have enough processing power and you can represent the processes that you have essentially models of the processes that constitute you. In other words, you're able to look at what you're thinking. You may have some sense of self -awareness. There's a bit of an act of faith on that many AI researchers don't buy that definition. There's a difference between self -awareness and actual raw intelligence.

46:37You can imagine a super powerful computer that would predict everything that was going to happen around you and was not aware of itself as an entity. The fact remains, you do need to have a minimal level of intelligence to be able to be self -aware. So a fly may not be self -aware. It just goes and finds good smelling poop and does whatever it's programmed to do on that. But dogs have some self -awareness and awareness of their surroundings. They don't have perfect self -awareness. They don't recognize themselves in the mirror and they'll bark at that. Birds will recognize themselves in mirrors.

47:19We recognize ourselves in many, many ways. So there is some correlation between intelligence and self -awareness. but these are not necessarily dependent functions. So what I'm hearing you say is, look, there are clear and present dangers associated with current best AI tools in that humans can use them for nefarious purposes. It seems to me that the most scalable example of that is still relatively small in that it's not existential threat to our species large, correct? Well, yes and no. If I was trying to do gain a function research with a virus, it's a good point. I could use these tools very effectively.

48:02Yeah, that's a great example. There's a disconnect and perhaps you understand the disconnect better than I do. There's those real existential threats. Then there's this more fuzzy thing that we're worried about, correctly about bias, incorrect decisions, hallucinations, we can get into what that might be, and our use in the every day of human conviction. And there's concerns about mistakes that might be made, there's concerns about displacement of workers that just as automation displaced the whole other series of workers. Now that we have something that works in the knowledge industry automatically, just as we're placing a lot of copy editors and illustrators with AI, where's that going to stop?

48:50It's now much more in the white collar space. And so there is concern around the harm that could be generated there. In the medical domain, are we getting good advice? Are we getting bad advice? Whose interests are being optimized in these various decision procedures? That's another level that doesn't quite rise at all to the level of extinction events, but a lot of policymakers and the public seem to be concerned about it. Those are fair points. Let's now talk about that state of play within medicine. So I liked your first example, almost one we take for granted, but you go and get an EKG at the doctor's office.

49:25This was true 30 years ago, just as it is today. You get a pretty darn good readout. It's going to tell you if you have an AV block, it's going to tell you if you have a bundle branch block. But in this way, they read EKG's better than I do. I'm not saying much anymore, but they do. What was the next area where we could see this? It seems to me that radiology is a field of medicine, which is, of course, image pixel -based medicine that would be the most logical next place to see AI do good work. What is the current state of AI in radiology? in all the visual -based medical specialties, it looks like AI can do as well as many experts.

50:15So what are the image appreciation sub -specialties? Pathology when you're looking at slices of tissue under the microscope. Radiology where you're looking at X -rays or MRIs. Dermatology where you're that pictures of the skin. So in all those visual -based specialties, the computer programs are doing by themselves as well as many experts. But they're not replacing the doctors because that image recognition process is only part of their job. Now, to be fair, to your point, in radiology, we already today, before AI in many hospitals, would send X -rays by satellite to Australia or India, where they would be read overnight by a doctor or a specially trained person who had never seen the patient, and then the reports filed back to us because they're 12 hours away from us, overnight would have the results of those reads.

51:19And that same kind of function can be done automatically by AI. So that's replacing a certain kind of doctor. Let me dig into that a little bit more. Let's start with a relatively simple type of image such as a mammogram or a chest x -ray. It's a single image. I mean, I guess with a chest x -ray, you'll get an AP and a lateral. Let's just say you're looking at an AP or a single mammogram. A radiologist will look at that. A radiologist will have clinical information as well. So they will know why this patient presented in the case of the chest x -ray for example in the ER in the middle of the night where they short of breath Do they have a fever?

51:57Do they have a previous x -ray? I can compare it to all sorts of information Are we not at the point now where all of that information could be given to the AI to Enhance the pre -test probability of whatever diagnosis it comes to I am delighted when you say pre -test probability Do you talk dirty around me? Love my bass theorem over here. Yep. So you just said a lot because what you just said actually went beyond what the straight convolutional neural networks would do. Because they actually could not replace radialitors because they could not do a good job in taking into account the previous history of the patient.

52:37And it's required the emergence of transformers where you can have multimodality of both the image and the text, now they're going to do better than many, many radiologists today. There is, I don't think, any threat yet to radiologists as a job. One of the most irritating to doctors, predictions was by Jeffrey Hinton, one of the intellectuals leaders of neural network architecture. He said, I think it was in 2016, I have this approximately wrong, but in six years we and have no need for radiologists. And that was just clearly wrong. And the reason it was wrong is, A, they did not have that. At least capabilities that we just talked about, about understanding about the clinical context.

53:22But it's also the fact that we just don't have enough radiologists. Meaning to do the training? To actually do the work. So if you look at American medicine, I'll let you shut me down. But if you look at the residency programs, Because we're not getting enough radiologists out. We have an over -abundance of applicants for interventional radiology. They're making a lot of money. It's high prestige. But straight up radiology readers, not enough of them. Primary care doctors, I go around medical schools and ask who's becoming a primary care doctor, almost nobody. So primary care is disappearing in the United States.

54:01In fact, mass general and brigham announce officially they're not seeing primary care patients. People still going to dermatology and they're still going to plastic surgery. What I did, pediatric endocrinology, half of the slots nationally are not being filled. Pediatric developmental disorders like autism, those slots, half of them filled, PDID, there's a huge gap emerging in the available expertise. So it's not what we thought it was going to be that we had a surplus of doctor that had to be replaced. It's just we have a surplus in a few focused areas which are very popular. And then for all the work of primary care and primary prevention, kind of stuff that you're interested in, we have almost no doctors available.

54:50Yeah, let's go back to the radiologist for a second because again, I'm fixated on this one because it seems like the most Well, the closest one to address and again if you're saying look we have a dearth of imaging radiologists Who are able to work the emergency rooms, urgent care clinics and hospitals? Wouldn't that be the first place we would want to apply our best of imaging recognition with our super powerful GPUs and now plug them into our transformers with our language models so that I can get clinical history, medical past history, previous images, current images, and I don't have to send it to a radiologist in Australia to read it, who then has to send it back to a radiologist here to check.

55:37Like, if we're just trying to fill a gap, that gap should be fillable, shouldn't it? And that's exactly where it is being filled. And what keeps distracting me in this conversation is that there's a whole other group of users of these AIs that we're not talking about, which is the patience. And previously none of these tools were available to patience with the release of GPT 3 .5 and 4. And now Gemini and Claude 3, they're being used by patience all the time in ways that we had not anticipated. it. Let me give you an example. So there's a child who was having trouble walking, having trouble chewing, and then started having intractable headaches.

56:20Mom brought him to multiple doctors, multiple imaging studies, no diagnosis kept on being an intractable pain. She just typed into GPT -4 all the reports and asked GPT -4, what's the diagnosis? GPT -4 said, Tethered cord syndrome. She then went with all the imaging studies to a neurosurgeon said, what is this? He looked at it, he said, Tethered cord syndrome. And we have such an epidemic of misdiagnosis and undiagnosed patients. Part of my background that I'll just mention briefly, I'm the principal investigator of the coordinating center of something called the Undiagnosed Network. It's a network with 12 academic hospitals down the West Coast from university in Washington, Stanford, UCLA, to Baylor, up the East Coast, Harvard hospitals, NIH.

57:10And we see a few thousand patients every year. And these patients have been undagnosed. And they're in pain. That's just a small fraction of those who are undagnosed. And yes, we bring to bear a whole bunch of computational techniques and genomics sequencing to actually be able to help these individuals. But it's very clear that there's a much larger burden out there of misdiagnosed individuals. But the question for you, Zach, which is, does it surprise you that in that example, the mother was the one that went to GPT for and inputted that? I mean, she had presumably been to many physicians along the way.

57:46Were you surprised that one of the physicians along the way hadn't been the one to say, gee, I don't know, but let's see what this GPT for thing can do. Most clinicians, I know do not have what I used to call the Google reflect. I remember when I was on the wards and we had a childhood dysmorphology, they look different. And I said to the fellows, this is after residency, what is the diagnosis? And I said, no, I don't know. I said, he has this and this and this finding. What's diagnosis? And I said, how would you find out? They had no idea. I just said, let's take what I just said and type it into Google.

58:24In the top three responses, there was the diagnosis. And that reflects what they do, use in a civilian life. They did not have in the clinic. And doctors are in a very unhappy position these days. They're really being driven very, very hard. And they're being told to use certain technological tools. They're being turned into data entry clerks. They don't have the Google reflex. They don't have the reflex who has a time to look up a journal article. They don't do the Google reflex. Even less do they have the let's look up the patient's history and see what GPT -4 would come up with. I was gratified to see early on doctors saying, wow, look, I just took the patient history, plugged into GPT -4 and said, right knee a letter of prior authorization.

59:14And they were actually tweeting about doing this, which on the one hand I was very, very pleased for them. because it was saving them five minutes to write that letter to the insurance company saying, please authorize my patient for this procedure. I was not pleased for them because if you use chat GPT, you're using a program that is covered by OpenAI as opposed to a version of GPT -4 that is being run on protected Azure Cloud by Microsoft, which is HIPAA covered. For those audience doesn't know, HIPAA is the legal framework under which we protect patient privacy. And if you violate it, you can be fined and even go to prison.

59:54So in other words, if a physician wants to put any information into GPT -4, they better not identify it. That's right. So they just plunked in a patient note into chat GPT. That's a HIPAA violation. If there's a Microsoft version of it, which is HIPAA compliant, it's not. So they were using it to improve their lives. The doctors were using it for improving the business, the administrative part of healthcare, which is incredibly important. But by and large, only a few doctors use it for diagnostic acumen. And then where about more involved radiology? So obviously a plane film is one of the more straightforward things to do, although it's far from straightforward as anybody knows who's stared at it, chest x -ray.

1:00:37But once we start to look at three -dimensional images, such as cross -sectional images, CT scans, MRIs, or even more complicated images like ultrasound and things of that nature, what is the current state of the art with respect to AI in the assistance of reading these types of images? So that's the very exciting news, which is, remember how I said it was important to have a lot of data, one of the three ingredients that breakthrough. So all of a sudden having a lot of data around, for example, echocardi grams, the other sounds of your heart. Normally it takes a lot of training to interpret those images correctly.

1:01:18So there was a recent study from the echoclip group led, I think, out of UCLA, and they took a million echocardi grams and a million textual reports. And essentially trained the model both to create those embeddings I talked about of the images and of the text. Just to make sure people understand what we're talking about, this is not, here's a picture of a cat, here's a description cat. When you put the image in, you're putting a video in. Now, you're putting a multi -dimensional video because you have time scale, you have Doppler effects, This is a very complicated video that is going in. It's a very complicated video and it's three -dimensional and it's weird views from different angles.

1:02:08And it's dependent on the user. In other words, the tech, the radiology tech can be good or bad. If I was the one doing it, it would be awful. The echo tech does not have medical school debt. They don't have to go to medical school. They don't have to learn calculus. You don't have to learn physical chemistry, all the hoops that you have to go through in medical school. You don't have the added to your little debt of doctors. So in two years they get all those skills and they actually do a pretty good job. They do a fantastic job, but my point is their skill is very much an important determinant of the quality of the image.

1:02:44Yes. But what we still require these days is a cardiologist to then read it and interpret it. Right, that's sort of where I'm going, by the way, is we're going to get rid of the cardiologist before we get rid of the technician. We're on the same page. My target in this combination is nurse practitioners and physician assistants with these tools can replace a lot of expert clinicians. And there is a big open question, what is the real job for doctors in 10 years from now? And I don't think we know the answer to that because you've fast forwarded the conversation just now. Excellent. Well, let's think about it.

1:03:20We still haven't come to proceduralists. So we still have to talk about the interventional radiologist, the interventional cardiologist, and the surgeon. We can talk about the role of the surgeon and the DaVinci robot in a moment. But I think what we're doing is we're kind of identifying the pecking order of physicians. And let's not even think about it through the lens of replacement. Let's start with the lens of augmentation, which is the radiologist can be the most easily augmented, the pathologist, the dermatologist, the cardiologist who's looking at echoes and EKGs and stress tests. People who are interpreting visual data and using visual data will be the most easily augmented.

1:04:02The second tranche of that will be people who are interpreting language data plus visual data. Now we're talking about your internist, your pediatrician, where you have to interpret It's important to remember that we're at symptoms and combine them with laboratory values and combine it with a story and an image. Is that a fair assessment in terms of tier? Absolutely a fair assessment. My only quibble, it's not a quibble, I'm going to keep on going back to this, is in a place where we don't have primary care. The American Association of Medical Colleges estimates that by 35, that's only 11 years from now, we'll be missing on the order of 50 ,000 primary care doctors.

1:04:37As I told you, I can't get primary care at the Brigham or an MGH today. And in the absence of that, you have to ask yourself, how can we replace these absent primary care practitioners with nurse practitioners with physician assistants augmented by these AIs? Because there's literally no doctor to replace. So Tommy, Zach, where are we technologically on that augmentation? If Nvidia never came out with another chip, if they literally said, you know what, we are only interested in building golf simulators and we're done with the progress of this, and this is as good as it's going to get. Do we have good enough GPUs, good enough multi -layer neural networks that all you need is more data in training sets that we could now do the augmentation that has been described by us in the last five minutes?

1:05:33The short answer is yes, let me give you very concrete. Most concierge services cost in the Boston somewhere between five and $20 ,000 a year. You can get this very low cost a concierge service that I'm just amazed that have not done the following called one medical. One medical was acquired by Amazon and they have a lot of nurse practitioners in there and you can make an appointment and you the right EKGs, the right medications, and assess your continuing heart failure. And only decide in the very few cases that you need to see a specialist cardiologist or a specialist endocrinologist today, just to be a matter of just making the current models better evaluating them, because not all models are equal.

1:06:28A big question for us, this is the regulatory question, which is which ones do a better job and they're not all equal. I don't think we need technological breakthroughs to just make the current set of paraprofessionals work at the level of entry -level doctors. Let me quickly say the old very bad joke. What do you call the medical student who graduates at the bottom of his class, doctor? And so if you could just merely get the bottom 50 % of doctors to be as good as a top 50 % that will be transformative for healthcare. Now there are other superhuman capabilities that we can go towards and we can talk about if we want.

1:07:13That do require the next generation of algorithms, Nvidia architectures and data sets. Everything stopped now. We could already transform medicine. It's just a matter of the sweat equity to create the models, figure out how to include them in the workflow, how to pay for them, how to create a reimbursement system, and a business model that works for our society, but there's no technological barrier. In my mind, everything we've talked about is take the best case example of medicine today, and augment it with AI such that you can raise everyone's level of care to that of the best, no gaps, and it's scaled out.

1:08:00Okay, now let's talk about another problem, which is where do you see the potential for AI in solving problems that we can't even solve on the best day at the best hospitals with the best doctors. So let me give you an example. We can't really diagnose Alzheimer's disease until it appears to be at a point that for all intents and purposes is irreversible. Maybe on a good day we can halt progression really, really early in a patient with just a whiff of MCI mild cognitive impairment, maybe with an early amyloid detection and an anti -amyloid drug. But is it science fiction to imagine that there will be a day when an AI could listen to a person's voice, watch the movements of their eyes, study the movements of their gate, and predict 20 years in advance when a person is staring down the barrel of a neurodegenerative disease, and act at a time when maybe we could actually reverse it?

1:09:05How science fictiony is that? I don't believe it's science fiction at all. Do you know that looking at retinas today, images of retina, straightforward convolutional neural network, not even ones that involve transformers, can already tell you by just looking at retina, not just whether you have retinal disease, but if you have hypertension, if you're a male, if you're female, how old you are, and some estimate of your longevity. And that's just looking at the back of your eye and and has seen enough data. I was a small player in a study that appeared in nature in 2005 with Bruce Yankter. We were looking at frontal lobes of individuals who had died of varied reasons, often accidents of various ages.

1:09:50And we saw that news for people like me that after age 40, your transcriptome, the genes that are switched on, fell off a cliff. 30 % of your transcriptome went down. And so it seemed to be a big difference in the expression of genes around age 40, but there was one 90 year old who looked like the young guy, so made this hope for some of us. But then I thought about it afterwards, and there were other things that actually have much smoother functions, which I don't have quite the follow up, like our skin. So our skin ages, in fact all our organs age, and the age at different rates. You're saying that the transcriptome of this skin, you did not see this cliff -like effect at a given age the way you saw it in the frontal cortex.

1:10:34So different organs age at different rates, but having the right data sets and the ability to see nuances that we don't notice makes it very clear to me that the early detection part, no problem, can be very straightforward. The treatment part, we can talk about it as well, but again, we had early on from the very famous Framianne Heart Study, a predictor of when you had going to have heart disease based on just a handful of variables. Now we have these artificial intelligence models that based on hundreds of variables can predict various other diseases. And it will do Alzheimer's, I believe, very soon.

1:11:18I think you'll be able to see a combination of gate, speech patterns, picture in your body, picture is skid and eye movements. Like you said, will be a very accurate predictor. We just published about, hey, recently, speaking about eyes, a very nice study where in a car, just by looking at the driver, it can figure out what your blood sugar is because diabetics previously have not been able to get driver licenses sometimes because of the worry about them passing out because of hypoglycemia. So there was a very nice study to show that you just by looking at cameras pointed at the eyes, could actually figure out exactly what the blood sugar is.

1:11:58So that kind of detection is, I think, fairly straightforward. It's a different question about what you can do about it. Before we go to the what you can do about it, I just wanna go a little deeper on the predictive study. You brought up the Framingham model or the multi -ethnic study on ethoscores as the Mesa model. These are the two most popular models by far for looking at a majorized versus cardiac event risk prediction. But you needed something else to build those models, which was enough time to see the outcome. In the Framingham cohort, which was the late 70s and early 80s, you then had the Framingham offspring cohort.

1:12:31And then you had to be able to follow these people with their LDLC and HDLC and triglycerides and later eventually they incorporated calcium scores. So if today we said, look, we want to be able to predict 30 -year mortality, which is something no model can do today. This is a big pet peeve of mine is we generally talk about cardiovascular disease through the lens of 10 -year risk, which I think is ridiculous. We should talk about lifetime risk, but I would settle for 30 -year risk, frankly. And if we had a 30 -year risk model where we could take many more inputs, I would absolutely love to be looking at the retina.

1:13:11I believe, by the way, it's a fact that retinal examination should be a part of medicine today for everybody. I would take a retinal exam over a hemoglobin A1C all day, every day. I'd never look at another A1C again if I could see the retina of every one of my patients. But my point is, even if effective today, we could define the data set and let's overdo it and we can prune things later. But we want to see these 50 things in everybody to predict every disease. Is there any way to get around the fact that we're going to need 30 years to see this come to fruition in terms of watching how the story plays out.

1:13:47Or are we basically going to say, no, you're, we're going to do this over five years. It won't be that useful because a five year predictor basically means you're already catching people in the throws of the disease. I'll say three words, electronic health records. So that turns out not to be the answer in the United States. Why? Because in the United States, we move around. We don't stay in any given healthcare system that long. So very rarely will I have all the measurements made on you, Peter, all your glycoheemoglobin, all your blood pressures, all your clinic visits, all the imaging studies that you've had.

1:14:23However, that's not the case in Israel, for example. In Israel, they have these HMOs, Health Maintenance Organizations. And one of them, Cladit, I have a good relationship with because they published all the big COVID studies looking at the efficacy of the vaccine. And why could they do that? Because they had the whole population available. And they have about 20, 25 years worth of data on all their patients in detail and family relationships. So if you have that kind of data and Kaiser Permanente also has that kind of data, I think you can actually come close. But you're not going to be able to get retina, gate, voice, because we still have to get those prospectively.

1:15:09I'm going to claim that there are proxies, rough proxies, but for gate, falls, and for hearing problems, visits to the odd job list. Now, these are noisier measurements. And so those of us who are data junkies like I am always keep mumbling to ourselves perfect is the enemy of good. waited 30 years to have the perfect data set is not the right answer to help patients now. And there are things that we could know now that are noble today that we just don't know because we haven't bothered to look. Give you a quick example. I did a study of autism using electronic health records maybe 15 years ago.

1:15:52And I saw there was a lot of GI problems. And I talked to a pediatric expert and they said, it was a little bit dismissive. They said, brain bad tummy hurt. I've seen a lot of inflammatory bowel disease. I just doesn't make sense to me that this is somehow a fact of brain function. To make a long story short, we did a massive study. We're looking forward to tens of thousands of individuals and sure enough we found subgroups of patients who had immunological problems associated with their autism and they had type one diabetes, inflammatory bowel disease, lots of infections. Those were noble but they were not known.

1:16:26And I had, frankly, parents coming to me more thankful that for anything else I had ever done for them, politically, because I was telling these parents, they weren't loosening that these kids had these problems. They just weren't being recognized by medicine because no one had the big wide angle to see these trends. So without knowing the field of Alzheimer's, the way I do other fields, I bet you there are trends in Alzheimer's that you can pick up today by looking at enough patients that that you'll find some that have more frontal temporal components, some that have more effective components, some that have more of an infectious and immunological component.

1:17:03Those are noble today. Zach, you've already alluded to the fact that we're dealing with a customer, if the physician is the customer, who is not necessarily the most tech -forward customer. and truthfully like many customers of AI runs the risk of being marginalized by the technology if the technology gets good enough. And yet you need the customer to access the patient to make the data system better, to make the training set better. So how do you see the interplay over the next decade of that dynamic? make. That's the right question because in order for these AI models to work, you have a lot of data, a lot of patients.

1:17:49Where is that data going to come from? So there are some healthcare systems which like the Mayo Institute who think they can get enough data in that fashion. There are some data companies that are trying to get relationships with healthcare systems where they can get de -identified data. I'm betting on something else. There There is a trend where consumers are going to have increased access to their own data. The 21st century Cures Act was passed by Congress and it said that patients should be given access to their own data programmatically. Now they're not expecting your grandmother to write a program to access the data programmatically, but by having a right to it, it leaves others to do so.

1:18:32So for example, Apple has something called Apple Health. It has this big heart icon on it. If you're one of the 800 hospitals that they've already hooked up with, past general or a Brigham women's and you're a patient there, if you authenticate yourself to it, if you give it your username and password, it will download into your iPhone, your labs, your meds, your diagnoses, your procedures, as well as all the wearable stuff, your blood pressure that you get as an outpatient, and various other forms of data. That's already happening now. There's not a lot of companies that are taking advantage of that, But right now that data is available on tens of millions of Americans.

1:19:10Isn't it interesting, Zach, how unfriendly that data is in its current form? I'll give you just a silly example in our practice. So if we send a patient to lab core, Boston Hard, or pick your favorite lab, and we want to generate our own internal reports based on those where we want to do some analysis on that, layout, trend sheets. We have to use our own internal software. It's almost impossible to scrape those data out of the labs because they're sending you PDF reports, their APIs are garbage. Nothing about this is user -friendly. So even if you have the My Heart thing or whatever, the My Health thing come on your phone, it's not navigable, it's not searchable, it doesn't show you trends over time.

1:19:59like, is there a more user -hostile industry from a data perspective than the health industry right now? No, no, and there's a good reason why, because they're keeping you captive. But Peter, the good news is, you're speaking to a real nerd. Let me tell you two ways where we could solve your problem. One, if it's an Apple health thing, someone can actually write a program, an app on the iPhone, which will take those data as numbers, and not have to scrape it, and it could run it through your own trending programs, you could actually use it directly. Also, Gemini and GPT -4, you can actually give it those PDFs, and actually, with the right prompting, it will actually take those data and turn them into tabular spreadsheets.

1:20:44We can't do that because of HIPAA, correct? If the patient gets it from the patient portal, absolutely, you can do that. The patient can do that, but I can't use the patient's data that way. If the patient gives it to you, absolutely. Really? Oh, yes, but it's not de -identified. Does matter. If a patient says Peter, you can take my 50 lab core reports for the last 10 years and you can run them through chat GPT to scrape it out and give me an Excel spreadsheet that will perfectly tabularize everything that we can then run into our model to build trends and look for things. I didn't think that was doable, actually.

1:21:23So, it's not doable through CHEG -PT because your lawyers would say, Peter, you're going to get a million dollars in fines from HIPAA. I'm not a shell for a Microsoft, I don't know any stock. But if you do GPT on the Azure Cloud that's HIPAA protected, you absolutely can use it with patient consent. 100 % you can do it. GPT is being used with patient data out of Stanford right now, epics using GPT -4 and it's absolutely legitimately usable by you. People don't understand that. We've now just totally bypassed OCRs. We do not need to waste our time for people not in the acronym's optical character recognition, which is 15 years ago what we were trying to do to scrape this data.

1:22:07Peter, let me tell you there's new in the Journal of Medicine. I'm on the editorial board And we just published three months ago a picture of them week back of this 72 -year -old and it looks like a bunch of red marks to me looks like something to scratch themselves and it says blah blah blah they had trouble sleeping. This is the image of the week. Image of the week and I took that whole thing and I took out one important fact and then gave it to GPT -4 the image and the text and it came up with the two things that thought it would be your bleomisin toxicity which I don't know what that looks like, and shittake mushroom toxicity.

1:22:43What I'd removed is the fact that guy had in mushrooms the day before. So this thing just like looking at the picture. GPT -4 spit this out? Yes. I don't think most doctors know this, Zach. I don't think most doctors understand. First of all, I can't tell you how many times I get a rash. Well, I try to send a picture to my doctor or my kid gets a rash and I'm trying to send to picture to their pediatrician and they don't know what it is and it's like we're rubbing two sticks together and you're telling me about the Zippo Liter. Yes. And that's what I'm saying is patients without primary care doctors.

1:23:20I know I keep repeating myself. They understand that they have a Zippo Liter waiting three months because of a rash or these symptoms. They say I'll use the Zippo Liter. It's better than no doctor for sure and maybe better. That's now quickly illustrated. I don't know squat about the FDA. And so I pull down from the FDA the adverse event reporting files. It's a big zip file, compressed file. And I said to GPT -4, please analyze this data. And it says, unziping based on this table, I think this is about the adverse events and this is the locations. What do you want to know? I say, tell me what adverse events for disease modifying drugs for arthritis.

1:23:59It says, oh, to do that, I'll have to join these two tables. And it just does it. It creates its own Python code. it does it and it gives me a report. Is this a part of medical education now? You're at Harvard, right? You're at one of the three best medical schools in the United States, arguably in the world. Is this an integral part of the education of medical students today? Do they spend as much time on this as they do histology where I spent a thousand hours looking at slides under a microscope that I've never once tried to understand? Again, I don't want to say there wasn't a value in doing that.

1:24:33there was and I'm grateful for having done it. But I want to understand the relative balance of education. It's like the stethoscope arguably we should be using things other than stethoscope. Let me make sure I don't get fired or at least been severely by sound telling you that George Daily, our dean of the medical school, has said explicitly he wants to change all of medical education so these learnings are infused throughout the four years. But it's going to take some doing. Let's now move on to the next piece of medicine. So we've gone from purely the recognition image based to how do I combine image with voice story text.

1:25:15You've made a very compelling case that we don't need any more technological breakthroughs to augment those. It's purely a data set problem at this point and a willingness. Let's now move to the procedural. Is there in our lifetimes? say, Zach, the probability that if you need to have a radical prostitutectomy, which currently, by the way, is never done open. This is a procedure that the Da Vinci, a robot, has revolutionized. There's no blood loss anymore. When I was a resident, this was one of the bloodiest operations we did. It was the only operation, by the way, for which we had the patients donate their own blood two months ahead of time.

1:25:54That's how guaranteed it was that they were going to need blood transfusions. So we just had to help with it come in a couple of months before give your own blood because you're going to need at least two units following this procedure Today it's insane how successful this operation is on a large part of the robot But the surgeon needs to move the robot Are we getting to the point where that could change? So let me tell you where we are today Today there's been studies where it's collected a bunch of YouTube videos of surgery and traded up one of these general models. So it says, oh, they're putting on the scalpel to cut this ligament.

1:26:32And by the way, that's too close to the blood vessel. They should move it a little bit to the side. That's already happening. Based on what we're seeing with robotics in the general world, I think the Da Vinci controlled by a robot 10 years is a very safe bet. It's a very safe bet. In some ways, 10 years is nothing. It's nothing, but it's a very safe bit. The fact is, right now, I can do a better job, by the way, just to go back to our previous discussion, giving you a genetic diagnosis based on your findings with them, any primary care provider, interpreting a genomic test. So, are you using that example, Zach, because it's a huge data problem?

1:27:16In other words, that's obvious that you would be able to do that because the amount of data, I mean, there's three billion base pairs to be analyzed. So of course, you're going to do a better job. But linking it to symptoms. Yeah, yeah. But you're saying surgery is a data problem because if you turn it into a pixel problem, pixel and movement and degrees of freedom. Yeah. That's it. Remember, there's a lot of degrees of freedom in moving a car around traffic. And by the way, lives are on the line. They're two. No, medicine is not the only job where lives aren't stake. driving a ton of metal at 60 miles per hour in traffic is also pretty life's of stake.

1:27:56And last time I looked at this, several manufacturers who are saying that or some appreciable fraction of that effort, they're controlling multiple degrees of freedom with a robot. Yeah, I very recently spoke with somebody, I won't name the company, I suppose, but it's one of the companies that's deep in the space of autonomous vehicles. And they very boldly stated, they made a pretty compelling case for it that if every vehicle on the road was at their level of technology and autonomous driving, you wouldn't have fatalities anymore. But the key was that every vehicle had to be at that level. I don't know if you know enough about that field, but does that sense check to you?

1:28:37Well, first of all, I'm a terrible driver. I am a better driver. It's not for ad, but in fact, I'm a better driver because I'm on a Tesla because I'm a terrible driver and this is actually a very good Message for medicine because I will paraphrase this I know enough to know that I need to jiggle the steering wheel when I'm driving with a Tesla Because otherwise it will assume that I'm just zoning out But I didn't realize is this I'm very bad. I'll pick up my phone and I'll look at it I didn't realize it was looking at me and says pick the exact put down the phone. So I okay I put down three minutes later.

1:29:10I pick it up again and it says okay, that's it I'm switching off autopilot. So I'm switching off autopilot and now I have to pay attention, full attention. Then I get home and it says, all right, that was bad. You do that four more times. I'm switching off autopilot until the next software update. And the reason I mentioned that is it takes a certain amount of confidence to do that to your customer -based saying, I'm switching off the thing that they bought me for. In medicine, how likely is it that we're going to fall asleep at the wheel if we have an AI thinking for us. It's a real issue. We know for a fact, for example, back in the 90s that doses force a drug like a dancer tron where people would talk endlessly about how frequently should be given up with what dose the moment you put it in the order entry system, 95 % of doctors would just use the default there.

1:30:00And so how in medicine are we going to keep doctors awake at the wheel? And will we dare to do the kind of challenges that I just described the car doing? So just to get back Back to it, I do believe because of what I've seen with autonomy and robots that as fancy as we think that is, controlling a dementia robot will probably have less that outcomes. Every once in a while someone nicks something and you have to go into full surgery or they go home and they die in the way home because they exaggerate. I think it's going to be safer. It's just unbelievable for me to wrap my head around that. But truthfully, it's impossible for me to wrap my head around what's already happened.

1:30:44So I guess I'll try to retain the humility that says, I reserve the right to be startled. Again, there are certain things that seem much easier than others. Like, I have an easier time believing we're going to be able to replace interventional cardiologists where the number of degrees of freedom, the complexity, and the relationship between what the image shows, what the calf shows, and what the input is, the stent, that gap is much narrower. Yeah, I can see a bridge to that. But when you talk about doing a Whipple procedure, when you talk about what it means to sell by sell, take a tumor off the superior mesenteric vessels, I'm thinking, oh my God.

1:31:25This is on record. I'm gonna say, I'm talking about your routine prostate removal. Yeah, for 10 years, I would take that bet today. Wow. Let's go one layer further. Let's talk about mental health. This is a field of medicine today that I would also argue is grossly underserved. Everything you've said to date resonates. I completely agree from my own experience that the resources and pediatrics and primary care, I mean, these things are unfortunate at the moment. Harvard has, I think, 6 % of undergraduates are getting some sort of mental health support and it's completely outdoing all the resources available to the University Health Services.

1:32:07And so we have to outsource some of our mental health and this is a very richly endowed university. In general, we don't have the resources. So here we live in a world where I think the evidence is very clear that when a person is depressed, when a person is anxious, when a person has any sort of mental or emotional illness, pharmacotherapy plays a role, but it can't display psychotherapy. You have to be able to put these two things together. And the data would suggest that the knowledge of your psychotherapist is important, but it's less important than the rapport you can generate with that individual.

1:32:43Now, based on that, do you believe that the most sacred protected, if you want to use that term, profession within all of medicine will then be psychiatry? I'd like to think that. If I had a psychiatric GPT speaking to me, I wouldn't think that it understood me. On the other hand, back in the 1960s or 70s, there was a program called Eliza, and it was a simple pattern matching program. It would just emulate what's called a regerian therapist, where I really hit my mother. Why do you say you hit your mother? Oh, it's because I don't like the way she fed me. What is it about the way she fed you? Just very, very simple pattern matching.

1:33:29And this Eliza program, which was developed by Joe Whites & Balma at MIT, A, his own secretary would lock herself in her office to have sessions with this thing, because it's non -judgmental. This was in the 80s? 70s or 60s. Wow. Yeah, and it turns out that there's a large group of patients who actually would rather have a non -human, non -judgmental person who remembers what they've said from last time shows empathy verbally. Again, I wrote this book with Peter Lee and Peter Lee made a big deal in the book about how GPT forwards showing empathy. In the book, I argued with him that this is not that big a deal And I said I remember from medical school being told that some of the most popular doctors are Papa because they're very deep empaths not necessarily the best doctors And so I said you know for certain things that's just me I could imagine a lot of for example what cognitive behavioral therapy being done and We found acceptable by a subset of human beings.

1:34:32It's not wouldn't be for me I'd say I'm just speaking to some stupid program But if it's giving you insight into yourself and it's based on the wisdom called for millions of patents, who's to say that it's worse and it's certainly not judgmental and maybe it'll bill less. So, Zach, you're born probably just after the first AI boom. You come of age intellectually, academically in the second and now in the mature part of your career when you're at the height of your esteem, you're riding the wave of this third version, which I don't think anybody would argue is going anywhere. As you look out over the next decade and we'll start with medicine.

1:35:20What are you most excited about and what are you most afraid of with respect to AI? Specifically with God to medicine, what I'm most concerned about is how it could be used by the medical establishment to keep things the way they are, to poor concrete over practices. What I'm most excited about is alternative business models. Young doctors who create businesses outside the mold of hospitals, hospitals are these very, very complex entities. They make billions of dollars, some of the bigger ones, but with very small margins, one have huge revenue, but very small margins, you're going to be very risk averse.

1:36:07And you're not going to want to change. And so what I'm excited about is the opportunity for new businesses and new ways of delivering to patients in sites that are data driven. What I'm worried about is hospitals doing a bunch of information blocking and regulations that will make it harder for these new businesses to get created. understandably, they don't want to be disrupted. That's the danger. In that latter case or that case that you're afraid of, Zach, can patients themselves work around the hospitals with these new companies, these disruptive companies and say, look, we have the legal framework that says, I own my data as a patient.

1:36:51I own my data. Believe me, we know this in our practice just because our patients own the data doesn't make it easy to get. There is no aspect of my practice that is more miserable and more inefficient than data acquisition from hospitals. It's actually comical. Absolutely comical, and I do pay hundreds of dollars to get my data from my patients with rare and unknown diseases in this network, extractive of the hospitals, because it's worth it to pay someone to do that extraction. Yeah, but now I'm telling you it is doable. So you're saying because of that, are you confident that the legal framework for patients to have their data coupled with AI and companies.

1:37:32Do you think that that will be a sufficient hedge against your biggest fear? I think that unlike my 10 -year prostateectomy by robot prediction, I'm not as certain, but I would give better than 50 % odds that in the next 10 years there'll be a company, at least one company that figures out how to use that that patience right to access through dirty APIs, using AI to clean it up, provide decision support with human doctors or health professionals to create an alternative business I am convinced because the demand is there. And I think that you'll see companies that are even willing to put themselves at risk, what I mean by that, are willing to take the medical risk on that if they do better than a certain level of performance, they get paid more.

1:38:23Or if they do worse. They don't get paid, yeah. I believe there were companies that could be in that space. But that is because I don't want to underestimate the medical establishments ability to squish threats. So we'll see. Okay, now let's just pivot to AI outside of medicine, same question. What are you most afraid of over the next decade? So maybe we're not talking about self -awareness in the sky net. But next decade, what are you most afraid of? And what are you most excited about? What I am most afraid of is a lot of the ills of social networks being magnified by use of these AIs to further accelerate cognitive chaos and vitriol that fills our social experiences on the net, it could be used to accelerate them.

1:39:20So that's my biggest fear. I saw an article two weeks ago that was an individual. I can't remember if they were currently in or formerly part of the FBI and they stated that they believed, I think it was somewhere between 75 and 90 % of quote unquote individuals on social media were not in fact individuals. I don't know if you spend enough time on social media to have a point of view on that. Unfortunately, I have to admit to the fact that my daughter, who's now 20 years old, been four years ago, she bought me a mug that says on it, Twitter addict. I spend enough time. I would not be surprised if some large fraction are bots could get worse.

1:39:58And it can be harder to actually distinguish reality from human beings, harder and harder. That's the real problem. We are fundamentally social animals, and if we cannot understand our social context, and most of our interactions, it's going to make us crazy. Or, I should say, crazy year. And my most positive aspect is, I think that these tools can be used to expand the creative expression of all people. If you're a poor driver, like me, I'm going to be a better driver. if you're a lousy musician, but have a great ear, you're gonna be able to express yourself musically in ways that you could not do before.

1:40:42I think you're gonna see filmmakers who were never meant to be filmmakers before, express themselves. I think human expression is going to be expanded because just like printing press allowed, all sorts of, in fact, it's good analogy because the printing press also created a bunch of wars because it made a lot of people to make clear their opposition to the church and so on, enabled a number of bad things to happen, but it allowed also the expression of all literature in ways that would have not been possible without the printing press. I'm looking forward to human expression and creativity. I can't imagine you haven't played with some of the picture generation or music generation capabilities of AI, or if you have a strongly recommend, you're going to be amazed.

1:41:23I have not. I am ashamed maybe to admit my interactions with AI are limited to really chat GPT -4 and basically problem solving, solve this problem for me. And by the way, I think I'm doing it at a very JV level. I could really up my game there. Just before we started this podcast, I thought of a problem. I've been asking my assistant to solve because, hey, I don't have the time to solve it. And I'm not even sure how I would solve it. It would take me a long time. I've been asking her to solve it. And it's actually pretty hard. And then I realized, oh my God, why am I not asking chat GPT -4 to do it?

1:41:54So I just started typing in the question. It's a little bit of an elaborate question. and as soon as we're done with this podcast, I'll probably go right back to it. But I haven't done anything creatively with it. What I will say is, what does this mean for human greatness? So right now, if you look at a book that's been written and someone who's wanna pull its surprise, you sort of recognize, like, I don't know if you read Sid Mukherjee, right? He's one of my favorite writers when it comes to writing about science and medicine. When I read something that Sid has written, I think to myself, there's a reason that he is so special.

1:42:31He and he almost alone can do something we can't do. I've written a book, it doesn't matter. I could write 100 books. I'll never write like Sid and that's okay. I'm no worse a person. I'm no worse a person than Sid, but he has a special gift that I can appreciate. Just as we could all appreciate watching an exceptional athlete or an exceptional artist or musician. Does it mean anything if that line becomes blurred? That's the right question. And yes, said the rights like poetry. Here's an answer which I don't like. I've heard many times. People said, Oh, you know that deep blue beat Kasparov and chess.

1:43:12But chess is more popular than it ever was, even though we know that the best chess players in the world are computers. So that's one answer. I don't like that answer at all. because if we create Sid GPT and Sid wrote Alzheimer's, the second greatest malady and it wrote it in full Sid style, but it was not Sid, but it was just as empathic, family references. Right. The weaving of history with story with science, yeah. If it did that and it was just a computer, how would you feel about it Peter? I mean, Zach, you are asking the jugular question. I would enjoy it, I think just as much, but I don't know who I would praise.

1:43:54Maybe I have in me a weakness slash tendency to want to idolize. You know, I'm not a religious person, so my idols aren't religious, but I do tend to love to see greatness. I love to look at someone who wrote something who's amazing and say, that amazes me. I love to be able to look at the best driver in history of Formula One and study everything about what they did to make them so great. So I'm not sure what it means in terms of that. I don't know how it would change that. I grew up in Switzerland in Geneva and even though I have this American accent, both my parents were a Poland and so the reason I have an American accent is I went to international school with a lot of Americans.

1:44:35All I read was whatever my dad would get me from England in science fiction. So I'm a big science fiction fan. So let me go science fiction on you to answer this question. It's not going to be in 10 years, but it could be in 50 years. You'll have idols. And idols will be yes, Greg Oralvich wrote a great novel, but you know AI521, their understanding of the human condition is wonderful. I cry when I read their novels. They'll be a part of the ecosystem. They'll be entities within us, whether they are a self -aware or not, will become a full self -acquestion. Let's not go that narrow path, that discussing rabbit hole where I wonder if just Peter actually have consciousness or not, does he have the same processes as I do?

1:45:17We won't know that about these. Or maybe we will, but will it matter if they're just among us and they'll have brands, they'll have companies around them, they'll be superstars. and they'll be Dr. Fubar from Kansas trained on Ayurvedic medicine, the Kate person for our alternative medicine, not a human, but we love what they do. Okay, last question. How long until from at least an intellectual perspective, we are immortal. So if I died today, my children will not have access to my thoughts and musings any longer. Will there be a point at which during my lifetime an AI can be trained to be identical to me, at least from a goalpost perspective, to the point where after my death, my children could say, Dad, what What should I do about this situation?

1:46:22And it can answer them in a way that I would have. It's a great question because that was an early business plan that was generated shortly after GPT -4 came out. In fact, I was talking very briefly to Mark Cuban because he saw GPT -4. I think he got trademarked or copyrights on his voice, all his work, and likeness so that someone could not create a mark who responded in all the ways he does. And I'll tell you that it sounds crazy, but there's a company called rewind .ai. And I have it running right now. And everything that appears on my screen, it's recording. Every sound that it hears, it's recording.

1:47:08And if characters appear on the screen, it'll OCR them. If it voice appears, and then if I have a question, I say, And when did I speak with Peter Ahtia? They'll find it for me. I'll say, who was I talking about? AI and Alzheimer's. And they'll find this video on a timeline. How many terabytes of data is this, Zach? Amazingly small. It's just gigabytes. How is that possible? Because A, it compresses it down in real time with using Apple Silicon. And second of all, you and I, you're old and you don't realize that gigabytes are not big on a standard Mac that has a Terabyte. That's a thousand gigabytes.

1:47:49And so you can compress audio immensely. It's actually not taking video. It's just taking multiple snapshots every time the screen changes by a certain amount. Yeah, it's not trying to get video resolution per se. No, and it's doing it. And I can see a timeline. It's quite remarkable. And so So that as enough in my opinion, data, so that with enough conversations like this, someone could create a pretty good approximation of at least public Zach. So then the next question is, is Zach willing to have rewind AI on a recording device, his phone with him 24 -7 in his private moments and his intimate moments when he is arguing with his wife when he's upset at his kids, when he's having the most amazing experience with his postdoc.

1:48:40If you think about the entire range of experiences we have from the good, the bad, the ugly, those are probably necessary if we want to formulate the essence of ourselves. You envision a day in which people can say, look, I'm willing to take the risks associated with that and there are clear risks associated with doing that. but I'm willing to take those risks in order to have this legacy, this data set to be turned into a legacy. I think it's actually pretty creepy to come back from the dead to talk to your children. So I actually have other goals. Here's where I take it. We are being monitored all the time.

1:49:15We have iPhones, we have Alexa devices. I don't know what is actually being stored by whom and what. And people are going to use this data in ways that we do or don't know. I feel it's us, the little guy, if we have our own copy. And we can say, well, actually, look, this is what I said then. Yeah, that was taken out of context. Yeah, that was taken out of context. And I can do it. I have an assistant that can just find it and find exactly and find all the time to send it. I think that's good. I think it's messing with your kids head to have you come back from the dead and give advice, even though they might be tempted.

1:49:51Technically, I think it's going to be not that difficult. And again, speaking about rewind AI, again, I have no stake in them. I think I may have paid them for a license to run on my computer. But the microphone is always on. So when I'm talking to students in my office, it's taking that down. So there are some moments in my life where I don't want to be on record. There are big chunks of my life that are actually being stored this way. Well, Zach, this has been a very interesting discussion. I've learned a lot. I probably came into this discussion with about the same level of knowledge, maybe slightly more than the average person, but clearly not much more on just the general principles of AI, the evolution of AI.

1:50:32I guess if anything surprises me and a lot does, but nothing surprises me more than the time scale that you've painted for the evolution within my particular field and your particular field, which is medicine. and I had no clue that we were getting this close to that level of intelligence. Peter, if I were you, this is not an offer because I'm too busy, but you're a cable guy and you have a great network. If I was running the clinic that you're running, I would take advantage of now. I would get those videos and those sounds and get all my patients with, of course, their consent to be part of this and to actually follow their progress.

1:51:15Not just the way to report it, but by their gates, by the way they look. You can do great things in what you're doing and advance the state of art. You're asking who's going to do it. You're doing some interesting things. You can be pushing the envelope using these technologies as just another very smart comprehensive assistant. So I keep giving me a lot to think about. I'm grateful for your time and obviously for your insight and years of dedication that have allowed us to be sitting here having this discussion. Thank you very much. It was a great pleasure. Thank you for your time. Thank you for listening to this week's episode of The Drive.

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1:54:53I'm gonna have to go back to the other side. I'm gonna have to go back to the other side. I'm gonna have to go back to the other side. I'm gonna have to go back to the other side. I'm gonna have to go back to the other side. I'm gonna have to go back to the other side. I'm gonna have to go back to the other side. I'm gonna have to go back to the other side. I'm gonna have to go back to the other side.