The Dr. Hyman Show - Dr. Eric Topol: Can AI Fix Our Health and Our Healthcare System?

Episode Date: February 14, 2024

View the Show Notes For This Episode Get Free Weekly Health Tips from Dr. Hyman Sign Up for Dr. Hyman’s Weekly Longevity Journal Get Ad-free Episodes & Dr. Hyman+ Audio Exclusives Dr. Eric Topol is the Founder and Director of the Scripps Research Translational Institute, Professor of Molecular Medicine, and Executive Vice-President of Scripps Research. He has published over 1,200 peer-reviewed articles with more than 330,000 citations, was elected to the National Academy of Medicine, and is one of the top 10 most cited researchers in medicine. His principal scientific focus has been on individualized medicine using genomic, digital, and AI tools. This episode is brought to you by Rupa Health, Pendulum, Thrive Market, and Fatty15. Streamline your lab orders with Rupa Health. Access more than 3,000 specialty lab tests and register for a FREE live demo at RupaHealth.com. Pendulum is offering listeners 20% off their first month’s subscription of Akkermansia for gut health. Visit PendulumLife.com and use code HYMAN. Head over to thrivemarket.com/Hyman today to receive 30% off your first order and a free gift up to $60. Fatty15 contains pure, award-winning C15:0 in a bioavailable form. Get an exclusive 10% off a 90-day starter kit subscription. Just visit Fatty15.com and use code DRHYMAN10 to get started. In this episode we discuss (audio version / Apple Subscriber version): The first phase of AI’s application in medicine (7:19 / 5:40) How medicine will incorporate AI over the next few years (9:59 / 8:19) Retinal imaging (16:45 / 15:05) Expert-informed AI (20:02 / 18:23) The impending and biggest shake-up in the history of medicine (22:18 / 20:38) How AI can improve issues of medical error (26:08 / 24:29) Using AI to personalize medicine and prevent disease (33:01 / 29:11) The obesity crisis and toxic food environment (41:04 / 37:14) How AI can help restore the patient-doctor relationship (46:53 / 43:03) Using AI as a guide for medical self care (50:34 / 46:44) Reimagining research in the era of AI-enabled medicine (56:58 / 53:07) Dr. Topol’s Substack, Ground Truths. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again

Coming up on this week's episode of The Doctor's Pharmacy. It's going to be, you know, the biggest shake-up in the history of medicine. The question is how we adapt. Hey everyone, it's Dr. Mark. We all know that as functional medicine practitioners, our time is precious. So imagine having more time to focus on what truly matters, helping your patients achieve optimal health. Arupa Health is here to make that a reality.

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I'm Dr. Mark Hyman, and this is a place for conversations that matter. Every day we're hearing about new advancements in the world of artificial intelligence. It's certain to have really profound impacts on almost every aspect of our life, including the way we practice medicine and the way we as individuals can gain control over our own health, which is exactly what my conversation today is about with Dr. Eric Topol. Dr. Topol is a giant in the field of medicine. Dr. Topol is the founder and director of the Scripps Research Translational Institute. He's a professor of molecular medicine and executive vice president of Scripps Research.

He's published over 1,200 peer-reviewed articles with more than 330,000 citations. And he was also elected to the National Academy of Medicine, which is a huge honor. And he is one of the top 10 most cited researchers in all of medicine. His principal scientific focus has been on individualized medicine

using genomic, digital, and AI tools. He's authored three best-selling books on the future of medicine, The Creative Destruction of Medicine, The Patient Will See You Now, and Deep Medicine, How Artificial Intelligence Can Make Healthcare Human Again. Topol is the principal investigator to two large NIH grants, the All of Us Research Program, which is really important. It supports precision medicine. It's over a million people tracking all their data, their phenotype, everything. It's going to give us so much

information. And he's also the recipient of the Clinical and Translational Award that promotes innovation in medicine. He was the founder of a new medical school at Cleveland Clinic, the Lerner College of Medicine, which is where I actually taught when I was working a lot at Cleveland Clinic. I'm still involved there as a senior advisor to the Center of Functional Medicine, and he's a legend there. He was commissioned to the UK to lead a review of the entire National Health Service, and he's actively working clinically as a cardiologist. God, I don't know how he does it all. Additionally, Topol is editor-in-chief of Medscape, which publishes the sub-sac newsletter Ground Truths and maintains a strong presence on social media and Twitter, or now known as X, with over 680,000

followers. Now, we begin our conversation talking about the need for change and the emergence of AI in medicine. Dr. Topol discusses how medical imaging and disease diagnosis has and will continue to benefit from AI and how we're moving into an era of keyboard liberation. Thank God for that. I'm so sick of being on my keyboard with patients. We talk specifically about some incredible advances in retinal imaging and AI's ability to detect early signs of disease that even an ophthalmologist trained at the best school, the best ophthalmologist in the world would never even see because he didn't even know it was there.

And AI identifies these things. Dr. Topol speaks to the challenges that will certainly arise as the field of medicine works to implement the incredible amounts of data that AI is soon to provide. It's no secret that we have a broken healthcare system and Dr. Topol reveals what he believes will be the most exciting thing about the incorporation of AI into medicine, as well as how it will likely be the key

to repairing the patient-doctor relationship. We also discuss how individuals will gain control over their own health as more and more AI tools come online, as well as how scientific research will be re-imagined in the era of AI-enabled medicine. Whether you're fascinated or skeptical about AI, this is a mind-opening conversation that explores how AI has the potential to revolutionize personalized healthcare and reestablish a more humane approach to patient care. Now, let's dive into my conversation

with Dr. Eric Topol. Well, welcome to the Doctors Pharmacy Podcast, Eric. It's great to have you, and I'm really inspired by your work. I think everybody heard from your bio, what you've done, and it's really at the forefront of everything that's happening in medicine, and you keep doing that. So thanks for being on the Doctors Pharmacy Podcast. Well, thanks for having me, Mark. So your book that you wrote recently was, the latest book is called Deep Medicine, How Artificial Intelligence Can Make Healthcare Human Again. And everybody's hearing about AI and the South Maltman and open AI and the dangers of AI and, you know, the controversies, the concerns.

And, you know, I've been kind of dreaming of this moment in healthcare where we actually could make sense of human biology with the help of artificial intelligence because it's so complex. The human body has got 37 trillion cells. There's 37 billion trillion chemical reactions that happen in the body every second. And, you know, when you go to the doctor, you basically get your panel of 20 or 30 or 50 analytes. He does, he pokes around, listens to your heart, lungs, and maybe does a little x-ray here or there, sticks a scope in. But it's really the dark ages compared to where we are headed. And you're at the forefront of understanding the intersection of the rapid changes in our understanding of human biology and the omics revolution and

systems medicine with technology and with artificial intelligence. So, you know, I think, you know, most people don't understand that doctors are working in an analog way, even though we have electronic medical records, the way we practice medicine really hasn't changed much in 150 years. And we don't use computers to help us sort through data, make decisions, and figure out what to do for our patients. And there are now examples of this. Maybe you can start by talking about how did you first get interested in artificial intelligence in medicine?

And how do you see it having made change already with certain things like imaging or retinal scans or interpretations of EKGs that are helping us understand and diagnose diseases better and earlier. And then we'll talk a little bit later about where do we see all this going? Because right now we're doing this sort of medicine better, but is it only the right thing to be doing? Sure. Well, I think the interesting thing about the AI scene is it really didn't get real until, let's say, seven, eight years ago. And it really, for our space of medicine, it was confined to medical images, scans. And that was the deep learning phase of AI.

And it really has been formidable. That is just about every type of scan you can imagine, but pass slides, electrocardiograms, the retina, as you mentioned, skin lesions, they could be interpreted as well or better by machines that were trained with so-called supervised learning, meaning that, of course, you had to have thousands, tens of thousands, hundreds of thousands of images that were annotated by expert physicians. And then you could train a model to do better than humans. So that was really great. And, you know, back in 2019, when I wrote Deep Medicine, it was about that phase of deep learning.

That's like ancient history now, right? 2019. Yeah, I know. It's amazing how quickly that has gone. Yeah, really, Mark. But what's interesting is, you know, I wrote in the book that what we need is a new model, because we didn't have one that could take all the layers of what makes us unique. You know, you've alluded to that, not just electronic health record, but our genome, you know, our gut microbiome, our sensors, our environment, our immunome, the works, right? And the fact that that data changes over time, and the fact that we could get the corpus of medical knowledge into that as well. So that's where we are now with this transformer model, also known as large language model phase, which is, of course, got a major jump in a year ago with CHAT-GPT, and now, of course, the about predicting a condition, better treatment, better prevention.

So we're on the cusp, but we haven't done it yet, to be honest. So, you know, no one has actually done multiple layers. They've done electronic health records and a genome, electronic health records and a scan. But to take multiple layers, including sensors, that's an analytical AI challenge that has yet to be solved. It will be imminently. And that's exciting. Yeah. I mean, you know, when you talk about it, you wrote an article that I thought was just so prescient and it was such a good description in a short amount of time and I encourage people to read it called As Artificial Intelligence Goes Multimodal, Medical Applications Multiply. And you talked about how we're going to be getting high dimensional data that underlie

the uniqueness of all of us and how it can be captured from all these different sources that you mentioned, including all the biomarkers we have through biosensors, wearables, implantables, our genome, our microbiome, our metabolome, our immunome, the transcriptome, proteome, every genome, it goes on and on. And then our electronic health records, our lab tests, our family histories, unstructured text from our medical records, and also things that are air pollution sensors we could be wearing. I just got one of those that someone sent me to try to wear to my air pollution, environmental stressors. All these things are going to be

then informed by the whole, you know, med line, a National Library of Medicine database of peer reviewed data. And it's going to create so much information. And it seems to me there's some intersection of a number of trends right now, which are going to transform medicine in a way that we can barely imagine. And it's going to happen very soon, which is the omics revolution, the systems biology and medicine revolution, the biosensors and wearable revolution, and then the AI machine learning learning, and big data analytic capacity that we have. And so those five basic trends are all converging in a way that I think is within even four or five years, we're going to see medicine be profoundly different because

the acceleration of this is happening so fast. And I'm excited about it because I feel like I've been trying to, with my little brain, put my head around all these immense complexity of human biology, which we've managed to navigate through this reductionist model of medicine and science into siloed specialties where you're a super sub, sub, sub, sub specialist on X, Y, or Z topic, but you don't understand how it all connects and interacts. And so the first time with AI, it seems like we're going to be able to do that. So how do you see this unfolding? And how is this kind of happening? And where are we going? Because I feel like I'm sitting on the edge of my seat. And right now, I feel like we're about to kind of

get out of our little dark ages and enter into an era where we're going to be able to make a real transformation in people's health. Well, I think you're right. It's extraordinary, this convergence that you're getting at. And it's going to happen in phases. So the first one is more of the practical, which is what I've been calling keyboard liberation. Yeah. Thank God. I heard that you say that. I'm like, hallelujah, because every doctor is stuck on their keyboard looking at the computer instead of looking at the patient. And that's going to be basically history of data clerk function, because we're already seeing now in many health systems around the country that you can do all this through the conversation. The only adjustment you have to make, Mark, is to articulate the physical exam findings with the patient. But other than that, the notes are far superior than the ones that are pecked along.

And what's great is once you have that note digitized and it's got all the juice in it, two big things happen. One is that, of course, you could put in any format conducive for the patient, you know, in terms of educational level or language or, you know, whatever cultural meant. You could also, that patient has the audio file. So if they don't understand something in that note, they can link it right to the audio file, listen to it again. And you know how many patients that you see where they're confused or they don't remember things. But the other big thing is on the clinician side, instead of having to peck through all this stuff, the orders for new tests and labs and return appointments, prescriptions, billing, pre-authorization, it's all done. It's all done. It's all done. And the nudges to the patient subsequent about the things that were discussed, like blood pressure. Did you check what were the results? You know, the AI picks that up, gets it back to the physician.

You know, all these things are now automated. So that will in itself be welcome. You know, instead of the things that all clinicians want to hate, this is, I think, something that will be widely embraced. And as you know very well, Mark, there's a lot of concerns about confabulation, hallucination, but that doesn't apply here. I mean, the AI is not going to be making things up about this kind of thing. Do you have that in your office yet? Do you have that in your office? I've used it at Scripps Health where I have cardiology practice. They haven't used what I consider the best of these, but they have done a pilot. The largest

one is the Microsoft Nuance, but the company that I've advised is a Bridge Health, which is derived from University of Pittsburgh and Carnegie Mellon. But there's been several. I mean, there's about 20 of these out there in various testing. Sam, I want to get think this is inevitability because this is finally the payback for all these bad years of having to become data clerks. But it's just the beginning. You know, it's just one thing that's going to be remarkably different. And that helps us to care better, but it doesn't change what we're doing. In other words, you know, we're going to be able to read x-rays better and MRI imaging better and pathology reports better and EKGs better and retinal imaging that tells

us so much about a patient's health. And these are incredible advances that are going to create much more refinement and understanding of how to be precise in our diagnosis of patients. And that's going to up-level medicine for sure. But let me, can I just chime in one thing? Yeah. for sure. Can I just chime in on one thing? Because the retinal image is something that is extraordinary. So before we just pass over that, I just want to point out that the original task was to see if the AI could interpret the image as well as a clinician. But what wasn't envisioned is that the AI could see things that humans will never see. So with the retina, as you touched on, the ability to predict Alzheimer's disease, Parkinson's disease, five to seven years before there's any symptoms.

The issue of, of course, the hepatobiliary tract, kidney disease, cardiac risk, risk of, you know, across all systems, diabetes control, blood pressure control. Someday we will be taking pictures of our own retina and get it as a checkup with an AI. So it's pretty amazing. And of course, that extends to cardiograms and chest x-rays. Each of them, there's all this stuff that the AI can see, if you will, that humans will never see. So it's something- It's even better than humans, right? Yeah, yeah. I mean, this is why, you know, when I interviewed Jeff Hinton recently for the podcast, I do ground truths. He said, you know, he's worried about AI because it's getting advanced so quickly, but not

for medicine. He thinks this is the sweet spot. This is really where the good is extraordinary. I agree. I mean, you know, I remember in medical school, you had the ophthalmoscope and you had to look in someone's eye and you, okay, you learn about AV nicking and high blood pressure and diabetic retinopathy and macular degeneration. You could see all that stuff, but there wasn't a whole lot else you could kind of figure out, you know,

and if you were an ophthalmologist, you might have a few more refinements in your ability to see things. But what you're saying is you can see things like Alzheimer's. So how does it pick that up? What is it actually seeing and looking at, for example, for Alzheimer's? Well, you know, this goes back to when the realization was made. And that was when you showed the retina picture to ophthalmologists. And you say, is this retina from a man or a woman? They got it right 50% of the time. woman? They got it right 50% of the time. And the AI got it right 97% of the time. And the answer is, we don't really know. Okay.

That is, there's explainability work to, you know, define these so-called saliency maps to try to deconvolute the model. But as far as what is it picking up to see the risk of Alzheimer's or Parkinson's or hepatobiliary disease, it isn't clear. I mean, there's some aspects that have been determined, but basically because these models are so extraordinary in terms of what they've learned and this is all from deep learning this isn't even from you know this transformer model era so so can you just stop there for a second you're talking about deep learning transformer model can you just explain the sort of shift and what you're thinking and because i don't think most people understand what that is. Right. So what was the phase of AI that lit up the world that Jeff Hinn

and his colleagues like Jan LeCun and many others, they basically found that there was this ability to input data that was supervised. That is that for our purposes, it was labeled by experts, so-called ground truths. And so they put it, what they knew was the actual image interpretation and trained with tens of hundreds of thousands of these images so that the machine could see stuff. So this is a knowledge base or expert informed AI, right? Yeah. Yeah. So that really was, you know, deep neural networks. That was the story. It required a single task, unimodal. And then what happened, a Google team in 2017 discovered what they call transformer models. The title of the preprint, attention is all you need.

And basically it changed the attention from a single bit of information, like a word in a sentence, to basically the context of the entire sentence, or of course, much broader than that, what turned out to be unsupervised, putting in the entire internet, Wikipedia, 100,000 books, 200,000 books. So that's what the transformer model, large language model, generative AI era that we're in now. It didn't start when ChatGPT was released last year, but it actually was in incubation. It was being pursued about six years now, but it's now blossomed. And we basically have two big types of AI now, the old, if you will, the old and the new. Yeah. I mean, it just seems it's going to

accelerate the pace of medical discovery because, you know, if a simple retinol scan can pick up things that we didn't even know we were missing, you know, we didn't even know we didn't know. They were unknown unknowns, as Donald Rumsfeld said. And that's just the back of the eye. Imagine when we put in all these things that we just mentioned, the whole omics field, the biosensors, the, you know, your pictures of what you're eating, your movement pattern. I mean, it's just an enormous amount of data

that's going to pick up patterns in that data that we've never seen before and that are going to inform what's happening on a biological level that I think is going to redefine medicine, just as we sort of redefine physics from a Newtonian or a world is flat view to, you know, a quantum view to even, you know, beyond that, it's like, we're, we're kind of in that era of biology where, where, where, where we basically have a profound revolution that's going to upend medicine. And I, I'd love to hear your perspective on, as we sort of enter that era and we start learning these things and understand the body as a network, understand the body as a system instead of these siloed specialties. How do you see that shifting medicine, medical education,

medical practice, reimbursement? I mean, these are, these are, this is a massive shift. Well, it is seismic. It's, it's going to be a challenge because medicine, as you know, doesn't change easily. And then you throw in all these other practical matters like reimbursement and education, regulatory, trust, implementation. I mean, there's a long list here of challenges. So this isn't going to be easy, but it's going to be, you know, the biggest shakeup in the history of medicine. The question is how how we adapt, how we, you know, our problem at the moment. Outside of a practical thing like we discussed with the keyboard thing is to get things implemented. We've got to have compelling evidence.

And there's a dearth of that because just like you can't get thousands of doctors to annotate images, and that's why this new form, transformer model, doesn't require supervised learning. It's self-supervised. So it basically is the bypass to what was holding back medicine. But just like that problem, you know, we have the problem of lack of dedication to do prospective trials, whether they're randomized or not, but getting the compelling evidence, which basically says to everyone in the medical community, this, this is it, you know,. This is going to lead to better patient outcomes, better everything. And there's always going to be some risk, of course, but there's never going to be a total positive side of the story. But we, except for the gastroenterologists who have done 33

randomized trials of colonoscopy with machine vision, and a few other randomized trials in radiology that have been quite impressive, particularly mammography. There hasn't been much compelling evidence so far. Yeah, it's true. It's true. But on the other hand, you look at the amount of deaths that are caused by medical practice, probably a third or fourth leading cause of death are, you know, complications or reactions to drugs or medical errors. It's huge. And I was listening to Elon Musk talk about cars and AI and self-driven cars. And he says, you know, about what, 40,000 people in America die from car accidents every year. You know, what if that was reduced to 10,000? But, you know,

that's a dramatic drop, but still you're going to have some people dying from a self-driving car and are we willing to accept that? So I think that's really a point where we have to kind of understand the value proposition and understand that there is some risk, but the upside in terms of reducing our healthcare costs, the burden on our healthcare system is going to be profound. Well, you know, just to amplify what you just said, we as physicians don't tend to want to acknowledge the problem of medical errors. So I'm glad you brought it up. Because, you know, the Johns Hopkins study that was published earlier this year in a British medical journal, about 800,000 Americans are either dying or seriously disabled each year from diagnostic medical errors. So you would think we'd want to invest in ways to bring that number down to the lowest possible. But that hasn't been the way, you know, medicine has worked.

The medical error of diagnosis is something that's got to be confronted. Hey everyone, it's Dr. Mark. Now, sometimes when I'm traveling, I need snacks. I don't always snack, but I like to have healthy snacks available. But if I'm traveling, it often means I'm tempted to reach out for whatever's quick and available rather than what's healthy and good for me. But thankfully, Thrive Market has made it easy for me to order my favorite snacks online

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for 20% off. And now let's get back to this episode of The Doctor's Pharmacy. Yeah, I think you're right. I think, you know, when I think about what actually medicine is today, you know, you go see the doctor, they ask you a bunch of questions, they run a bunch of tests, it filters through their one little brain. They went to medical school,

I went to medical school in 1983, I don't know why I went to medical school. I've tried to keep up, and, you know, I've had my own narrow experience and, and that patient is relying on me to create all the associations, pattern recognition, sort of be able to sift through all the medical literature, whatever the 9 million published articles on PubMed, make it make sense of what's going on with them. It's kind of embarrassing. Like I feel like I'm, I'm kind of basically groping around in the dark and

I still obviously help people and obviously you do and doctors do a good job. But when you think about what's possible in terms of decision support for practitioners, it's not like the AI is going to be treating the patient, but it's sort of an AI assisted doctor where the doctor then will have to get all that data, then filter through their understanding and medical knowledge. And that decision support from the AI will help them create better outcomes, more personalized treatments, specific care, and really overall improve healthcare. So how do you see the AI decision support process happening, unfolding? Because I think this is where I'm most excited about. Well, this is happening really quickly. The proof of how the large language model exceeds the doctor's ability to make complex diagnoses. And what is an extraordinary

recent preprint, just a number of days ago, was from the Google research team where they took over 300 of the New England Journal clinical pathologic conferences, which are the master clinician trying to get the diagnosis of the really complex, challenging case. And what was amazing is that the large language model got the differential diagnosis randomized against 20 internists with nine years of experience. So these are not just rookies. These are experienced internists. The AI got twice the accuracy, twice, than the internist. And then when you gave the internist a Google search, it didn't make that much difference.

And even when you gave the internist then being able to go to GPT, well, it was a MedPOM 2 fine-tuned. We don't know exactly what it is, but it's some large language model. It still didn't get to the large language model alone level. So what we're seeing now, and that experience is getting replicated, that large language models do very well. Now, you and I, in seeing a patient, we wouldn't only rely on the output of the large language model, but what it does, especially in these challenging complex it could be rare conditions just very difficult you know cases um it what it does is it may bring to mind some conditions that

didn't pop into our into our head and so you you know it basically is this is an exciting thing to see and as you know there's been amazing anecdotes about how patients have put their symptoms. Yeah, that's true. Or a mother for her son, you know, put symptoms of after seeing 17 doctors for three years for a boy who is just, you know, debilitated and growth arrest. And then gets the diagnosis of occult spina bifida herself through chat GPT. I mean, this is happening now. You can't hold back patients from using. It's like a Google search, but why do a Google search

when you're going to get so much enriched information, which you have to verify, of course. Yeah, I mean, part of the challenge with this is also that it's going to pick up things that we didn't know and challenge the current model. You know, as we now know, you know, diseases aren't homogenous and our current diagnostic model is based on symptoms and pathology and geography. You know, what's the symptom, where is it in your body, and what does it look like under a microscope, not causes and mechanisms and not the infinite complexity of our whole phenome that we just described. And as we start to sort of ingest

the phenome into this AI monster, whatever you call it, or AI genius, it's going to come up with a lot of different associations and patterns that reflect a way to personalize medicine, to create precision, or as you say, individualized medicine. That's what excites me because, you know, Alzheimer's isn't uniform. Diabetes isn't uniform. If you have breast cancer, we know that we know this. I mean, years ago, I read a paper and I think a jammer in the journal was like, you know, the staging of cancer, breast cancer is far less predictive of the outcomes than if you look at the genetics of the cancer and that there's no such thing as breast cancer. They're breast cancers and they're all

incredibly heterogeneous. And so this is not just true for cancer, it's true for everything. And whether it's rheumatoid arthritis or whether it's cerebral bowel syndrome or whether it's migraine headaches or whatever the condition is. And we in medicine just treat them all the same. You've got a migraine, that's the diagnosis. And I think, you know, we're really good at this diagnostic, you know, differential diagnosis process. That's how we get trained in medical school. But it doesn't really take us down the next layer, which is looking at mechanisms and causes and personalization. So how do you see where we're headed in medicine sort of breaking that old paradigm and helping us create a more individualized sort of approach to different

diseases. You know, I'm really glad you brought this up because that's kind of the reductionist. You know, just because we just can't deal with this difficulty of heterogeneity of people, of diagnoses, of response to treatments and on and on, we basically dumb it down. And we just say, well, you got this condition or that condition. When, as you said, you know, things like type 2 diabetes, it's just type 2. There's about like 20 subtypes of type 2 and on and on too. So we will have this kind of pinpoint precision and accuracy, which will help promote, you know, very specific individualized treatment. And that will that can also extend to, you know, what would be the best optimal diet for any given person because, you know, we respond to food remarkably

differently. Everything we do, we are unique. Even two identical twins are unique. They have very different epigenomes and, you know, so we have to appreciate once we get to the ability to deal with the information, which we couldn't until now, and we're just starting to in many respects, then we can start to get to, once you recognize that each human is unique, then you can hopefully get to be much better at preventing, diagnosing, treating, getting better outcomes, and promoting healthspan. Yeah. I think it's so true, Eric. We in medicine wait until there's something to look at or do. And when your blood sugar goes up, or your blood pressure goes up, or you get a chest pain, your cholesterol goes up. But the truth is that disease is a continuum from optimal wellness and perfect health to this transition slowly over often decades to symptoms and then diseases. And in medicine, we're jumping in the game so late. And I think what AI and machine learning

is going to help us do as we start to ingest the phenome is to start to look at these patterns and these transitions from wellness to disease much earlier and be able to intervene earlier before there's ever a symptom. Like you mentioned, you can look at someone's eye and see what they're going to have Alzheimer's or look at various, even things like voice patterns or typing on a computer or all these things are actually signals that can be interpreted that we have no clue as doctors what to do with or how to make sense of. But through proper application of AI and medicine, we're going to be able to start to sort and sift through this, right? That's exactly right.

The fact that you could predict a condition before any symptoms ever manifest, and we have ways to prevent that condition to apply for that person. That's what makes almost unheard of, right? But, you know, secondary prevention, once you've had a heart attack, you know, prevent the second heart attack. Okay, sure. But primary prevention is going to be actualized in the era of AI for the conditions in which there's a way to truly prevent. So right now, yeah, I wouldn't say our Alzheimer's drugs that have recently been approved are

the end all to do that, but we're chipping away at that. Yeah, yeah, really. But we are going to have drugs and interventions that will prevent conditions like neurodegenerative conditions. We are going to have ways to prevent cancer, like the earliest sign, you know, to rev up the immune system, to squash it. Or, you know, to go into high mode surveillance to get frequent, you know, cell free tumor DNA blood samples and all sorts of ways to get on top of it before it ever would show up on a scan, before it ever could go to the chance of spread. So many diseases that we have to confront that are common will have an altogether different approach once you know that the person's

at risk. If you know a person's going to have asthma in their life, prevent that they ever have a wheeze. I mean, you know, heart failure, no, they're never going to have heart failure. So you just have to know what the person is at risk for and then have something on the menu that works. Yeah, I think it's true. And I think, you know, one of the things from my perspective is this whole idea of, I wrote a book called Ultra Prevention, you know, one of the things from my perspective is this whole idea of, I wrote a book called Ultra Prevention, you know, like 20 years ago, essentially was about this. It's like, well, we don't really have true prevention because we're just looking for things after they've occurred through a pap test or a mammogram or a, you know, or a colonoscopy

or whatever we're doing. And we're entering a year where we're going to be able to see these subtle changes and then learn how to modify them and tweak them and through diet and lifestyle and some of the preventive things that we know work in a much more personalized way. And I think one of the things about your work that I love is that you do talk about nutrition. Very few doctors talk about it. You're very focused on what that looks like. You recently published in November an article on your substack called Towards an Optimal Diet, looking at the role of diet, nutrition, and chronic disease. And for me, it's this best of worst of times moment.

We have the best of times with the advances in technology, all the exciting things we've been talking about that are going to revolutionize medicine. But at the same time, we're also seeing this explosion of chronic disease and diet-related illness is the number one killer in the world now. And we're seeing increasing mental health disorders and all sorts of burden of chronic disease that are exploding. At the same time, we're seeing all these advances in medicine. So how do we reconcile these two? It's like, how do we kind of use one to solve the other? Because I feel for me, this is the biggest problem that no one's really addressing is we're, we're, we have better, better healthcare than ever before. And we're getting sicker and dying younger than ever

before. And our life expectancy is going down. So you got to do something about it. Well, yeah. I mean, it isn't one simple thing, of course, there's lots of moving parts here on the one hand, you know, as you well know, we have a monstrous problem with obesity and diabetes and diabetes, the twin pandemics. And even though we're starting to chip away a little bit of that with these GLP-1 family drugs. Yeah, like Ozempic.

Yeah, you know, Manduro and then the triple receptors that will follow but you know we're only at the at the beginning of that and who knows how many people ultimately will have uh their weight uh issues addressed and of course the toll of obesity on every system in the body uh is is profound now at the same time, we ignore our environment. You know, yesterday, my friend Sid Mukherjee had a great article on the carcinogens and air pollution. And I actually think that ultra processed foods and a lot of other things that we take in in our foodstuff and beverages is potentially a liability for bad health, cancer, cardiovascular risk. We don't pay attention to it. I'd remove the word potentially.

Yeah. I think the data is so clear on this ultra processed food link. Actually, I shouldn't have used that word.'m glad we're we're in a accord yeah uh no not potentially the data is overwhelming but nobody does anything about it you know so the and what are we doing to get rid of the carcinogens and the things that are accelerating uh chronic major chronic diseases like you know like heart disease and cancer and neurodegenerative diseases so we've identified them but we do nothing about it and you know remember mark how long did it take to get trans fats to be you know outlawed but they're not even i mean even

the government has has ruled them as non-grass generally they're not recognized as safe to eat but they're still in grocery stores, even after almost eight years later, having that ruling, which is the food industry's lobbying efforts. Yeah. So, you know, this is amazing, right? So we, we know some of the culprits, but we don't do, we have no teeth. We have no, we do nothing about it. You know, Europe, they actually have done better on this than the U S so this is really disheartening. Yeah, I think it's true. I mean, I think we're going to link in the show notes to this article in your newsletter called Ground Truths. I love this. Ground Truths is like, you know, what are the fundamental laws of nature

instead of like how I think about it? What is sort of the indisputable truth? And I think we're getting close to understanding that link between all the diseases that we're seeing exploding and the things that we have control over, our diet, our lifestyle, sleep, stress, environmental toxins, things that we actually can modify that doctors actually pay very little attention to, right? Yeah. Well, you know, just look at, you know, you're well aware of this. How come people in their 20s now are getting colon cancer? How come people in their 30s who never smoked are developing lung cancer, breast cancer? You know, it isn't their genes. You know, what is what's going on here? And so you have to really start looking at things like our environment, which includes what we're eating, what we're breathing. And again, it's like a blind eye to this stuff.

It's true. It's shocking to me that Fatima Sanford from Harvard, who's a professor now in the U.S. Dietary Guidelines Committee, said on 60 Minutes that obesity is genetic. And I think there are genes involved for sure that put you at risk or predispose you. But, you know, where were all the, you know, obese people when you and I were born? You know, there was five, 10% obesity. Now it's 42%. And a hundred years ago, it was nothing. And, you know, it's kind of frightening to see that level of scientist who's now on a federal guidelines committee coming up with this concept and not talking about the real issue. And she's, you know, got funding from Mozambique and it's just like, oh my God, this is, it's so corrupt, you know, it reminds me and kind of where we are now in a way, I think it's a little pejorative for doctors, but I think it kind of reminds me of Voltaire's

saying where he said, doctors are men who prescribe medicines of which they know little to cure diseases of which they know less, and human beings of whom they know nothing. And I think, you know, we're going to look back maybe in five or 10 years and go, what the heck were we doing? What were we thinking? Because, you know, I feel like it's such an, like, I'm just sort of sitting on the edge of my seat. It's such an exciting time.

And I think if we can, if we can cross this threshold and we can get the paradigm to shift and we can use technology to do it, I think the world's going to look very different. I think our ability to treat disease is going to be very different. So tell me in that context, what are you most excited about in this? Is it just making healthcare improvements around how the process goes, around decision support, around healthcare costs, or is around really a scientific paradigm shift? Well, I mean, actually for me, we've touched on things that I think are really the big frontiers, not only including a virtual coach for people who want to use it to keep them healthy and and hospitals at home where you, unless you need

to be in the ICU, you don't ever go near the hospital because you can be monitored with multimodal AI. But to me, the far-reaching objective is to restore the patient-doctor relationship. Because, you know, I've watched this go down the tubes. I mean, you know, back when I finished med school and started training, postgraduate training in the early 80s, the relationship with a doctor and patient was a precious thing. I mean, you knew, patient knew that you were there for them, that when you met with them, you were there. You were present. You were present. Yeah. Yeah.

And, you know, you had their back and it was an intimate, compassionate, empathetic, trust relationship. What is it now? There's very little of that. I mean, you know, there are some exceptions, but there's not the time, you know, and that's why I really think this gift of time that AI can bring us, whereby, you know, we give exciting thing to derive from the AI era. We have a ways to go because right now things are definitely going in the wrong direction and we have shortage of clinicians. But ultimately, if we do this right, if we stand up to administrative overlords that tend to rule the roost, a lot of health systems and health practices around the country, then maybe we can get this on track.

Yeah, I think it's so important. And it also speaks to, you know, the need to help those who are not having access to health care and medicine, who don't have experts in their area. And, you know, I remember when I was a family doctor in Orofino, Idaho, in the middle of nowhere in a logging town. And we were it. There was like five family doctors. And so when you're on call and something came in and it was weird or strange, you had to deal with it. And so I remember I would call up the doctors in Spokane, Washington, which was the closest sort of major hospital. And I said, okay, what do I do? I got this baby. It's a premature birth. I got to put in this umbilical cath hospital. And I said, okay, what do I do? I got this baby. It's a premature birth.

I got to put in this, you know, umbilical catheter. And I would be coached through it. But, you know, I was a rural doctor back then. And there are now people, you know, globally, we're facing a doctor shortage, a nursing shortage, problems with accessing healthcare. And I think these technologies will actually help people in those areas, you know, end some of these health disparities and scale up medicine in a way that I think we haven't seen before. I really agree with that. I mean, we've got to do something to address, you know, the

inequities and they include not just, you know, socioeconomic, but, you know, rural versus urban, like you're getting at. And, you know, this is essentially software. And it doesn't have to be a problem to accentuate or exacerbate the gap, but it hopefully could actually reduce the problem, which is very, it's so severe that we have today. But I think this is something that we have an opportunity. We can see where this is headed if we really go after this. Right now, there's this big controversy about accelerating, decelerating this. It seems to me we need to be accelerating for the most part because there's too much good here that can be actualized. Particularly in medicine.

I think that's the key. And I think the doctor and the healthcare system is a bottleneck for people who are suffering. And not just taking time to get an appointment or lack of access, but most of the health issues we have, I believe, can be solved by self-care. If we had the right information, like you're mentioning, what are the foods that affect your blood sugar different than this person? And I think having a kind of AI-powered personal health co-pilot is going to be possible by ingesting this large phenomic data, all the stuff that I said at the beginning, all your, you know, lab tests, your wearables, your symptoms, and it goes

into a system that'll create some intelligent guidance for how you can handle this on your own, what to eat, how to, you know, exercise better, simple practices you can do. And I found that, you know, as a functional medicine doctor, you know, most better, simple practices you can do. And I found that, you know, as a functional medicine doctor, you know, most of my patients can get better from things without me. Like if they just had the roadmap and could follow it and do the self-care practices, because, you know, most conditions that are chronic illness often can be solved through, you know, really simple, basic lifestyle or other, other changes. And, and I think that's going to, in some ways, relieve pressure on the healthcare system. You can go to your think that's going to, in some ways, relieve pressure on the

healthcare system. You can go to your doctor when you need to, but right now it's almost like a guild. You have to go through the healthcare system to get your data. You don't own your data. You don't own, you can now, I wear an aura ring or a watch, you can get your biosensor data, but what about what's under the hood? You can't. And so now I think we're going to, I co-founded this company called Function Health, which is designed to be an AI-powered personal health co-pilot where all this data is going to be ingested and you'll be guided on what to do for self-care and then when to go get medical care and what kind of medical care you might need. And then it'll help the doctors have a decision support tool, but it's happening outside the

healthcare system because it's so hard to change the current paradigm. The Structure of Scientific Revolutions by Thomas Kuhn was the first discussion of this idea of paradigm shift. And he said how hard it is to change normal science, essentially what people are doing now. But I think through sort of coming from the outside, from the consumer-driven aspect of healthcare, I think we're going to see a greater acceleration of this.

And I'd love to hear your thoughts on that movement and that effort. Yeah, no, I think this is really interesting what you're bringing up. So firstly, there are now, oh, 20 or 30 companies, mostly startups, that are offering coaching chatbot with humans in the loop as a backup for most chronic conditions like hypertension, depression, diabetes, you know. So they are, some of them have done randomized trials to show they're really helping people. So, you know, we were talking about unimodal. So this is narrow. They're helping one condition. The next phase is what you are getting at, which is a virtual coach across all conditions to prevent them. And I don't see theoretically why that can't be done. It has to be proven that it's working. Now, one of the things that helped me in recent days to see, well, this is inevitable.

There was a paper by the Stanford Group in Nature about organ-specific clocks. It was to cover last week. You mean like aging clocks, you mean? Yeah, aging clocks. So you know how you can get a so-called Horvath or epigenetic clock to say what is your biological age, and hopefully it's similar or better than your chronological age. Well, mine was 43, and I'm 64, so I'm going with that. Oh, okay. There you go.

Good for you. Wow. Wow. Anyway, that isn't that helpful compared, I think, with organ-specific eat. So what they discovered, which is using AI. Yeah, my back is about 110. Right. But yeah, you didn't need the proteins to know that.

But what they discovered, which was amazing, and they validated through three different populations, was that there's signatures in proteins that can be assayed in each of these organs to know for you, what are you at risk for? Are you at risk for your kidney, your liver, your heart? And this is just yet another layer of data, which didn't have before to add to the mix so you know i do envision that this this virtual coaching you know with uh periodic whether it's retinal assessment or organ specific clocks or you know you name it um and what they showed in that study in the report in nature was the proteins correlated with what happened to people. That is, people with accelerated aging of their heart had heart attacks and heart failure.

People with brain aging, of course, went on to Alzheimer's. And so this is yet another way to anticipate an individual. You won't get that from an overall body clock, but you'll get it from, you know, organ clocks. And I think that to me was, you know, really one of many important in the chain of recent discoveries. Well, that speaks to the real issue here, right? Which is, what are we looking at as doctors? We're looking at, you know, basically things with a magnifying glass, not a microscope. And now we have an electron microscope, you know, to even look deeper. And I think, you know, there's tens and tens of thousands of molecules and proteins and metabolites and, you know, even peptides and things that are floating

around in your blood, many of them even from your microbiome. Stan Hazen said he thinks about a third of all the metabolites in your blood are from your microbiome. And we don't look at that. You know, if you do an extensive panel of tests, you might even get, you know, 50, 100 blood tests, but that's kind of miniscule when it comes to what's really going on. And so what you're talking about really is this on this precipice of being able to look at all of these analytes from all the different sources and be able to track what's going on in your body and use that information to optimize your health and to do research in a different way.

So can just the last few minutes we have, I'd love to sort of have you kind of pontificate on how we are going to reimagine research in this era of AI-enabled medicine? Well, I mean, I think the research has to be more to get a change in practice. So another really good example of the same major discovery, the Icelandic group publishes that when people in Iceland had their genomes assessed, they found that 4% could have had their lifespan extended up to seven years if they had known the information about, for example, BRCA2 mutation recently published in the New England Journal. So what I'm getting at, Mark, is we have abundant medical research that sits in these high tier journals, goes nowhere. Yeah. Never goes into patients.

Yeah. Never is proven in real patients in the real world that it changes their health. It promotes their health. That's what we need now to basically get this into high gear. We have too much stuff that's stuck in the, you know, what I think you recognize there's this paternalism in medicine. And, you know, patients don't tell them about their genomic data because, you know, they won't be able to handle the truth.

It's a little patronizing. It's a little patronizing. Yeah. It's their data. If they want to get their data and they want to know what caveats there are and, you know, so-called variants of unknown significance, that's their right, but we don't let them have their data. So we have to stop this, and that's holding us back. Yeah, it's true. And I think I just want to point out that you're the founder and director of the Scripps Research Translational Institute, which is really designed to translate from the bench to the clinic

research that's going to change people's lives. And we're so slow in medicine. I mean, everybody knows Mr. Osemo Weiss, who sort of figured out that we should wash our hands before attending to births. And he saw that the midwives patients were not dying of childbirth fever, and all the doctors' patients were. And he said, you know, guys, we should probably wash our hands. They're washing their hands. Maybe it's going to help. And they were like, God, how could you even suggest that doctors would be causing any harm to their patients? You're excommunicated and he died in disgrace. And it took 50 years to figure out that we should wash our hands before surgery. And it's kind of like that right now where the

advances are happening so rapidly in medical science and, and doctor, and my daughter's in medical school now. And I'm like, just so embarrassed. I said, Rachel, are you, are you learning about the microbiome? No. Did you learn about insulin resistance? No. Did you learn about nutrition? Well, we learned about amino acids and fatty acids. I'm like, what are you gonna tell your patient to eat? I was, I'm like, what is happening out there? So I just so excited by this moment because I think it's going to be a way to disrupt healthcare that we haven't been able to do yet. And I'm so excited that you've been thinking about this, leading this work. You know, you've done so much in your career that's advanced science and thinking so much.

And, you know, it really, it makes me very inspired to, to know people like you and who are at the peak of healthcare and, and understand these concepts and are trying to push medicine forward into the place where it can really be what it's designed to be a profession that really is there for helping and healing and transforming lives and not just a big medical industrial complex. Oh, thank you. Thanks, Mark. That means a lot. And, you know, healing, caring, those are the key. We got to get to that and do it better and, you know, leverage what we can out of AI and machines to help us get there. Yeah. What was that quote? The secret of caring for the patient. The care of the patient is caring for the patient. Francis Peabody, 1927. My favorite quote actually. Yeah, that's right.

That's exactly true. And it's such a beautiful sentiment. I think this revolution in medicine and AI and systems biology and all the things we're working on, I think are going to allow us to bring humanists back into medicine, which seems almost paradoxical given that it's technology. But I think it's going to allow a much more humane approach to our patients. And I can't wait to have keyboard liberation. Yeah, it's coming. It's going to happen fast.

You'll see. All right. Well, any final thoughts, Eric, on where we're headed and what you're inspired about next? Well, no, I'm just excited. I was talking to my fellow recently about how he's going to really see it because it's going to take some years. Every time I forecast something, I realize that my impatience tends to accelerate when we're going to really get there. But, you know, we will eventually. It will be a far better way that we practice medicine and patients will derive far more benefit.

They'll have renewed faith in physicians and the relationship they have with them. And, you know, this is really a unique time to look forward to. I hope we'll get there. The sooner, the better. Well, I think both of us obviously take care of our health and are focused on our health, so hopefully we're going to extend our life long enough we'll be alive when all this happens.

I hope so. Look forward to having more chats with you and following this along the way, and thanks for all your work, Eric. Thank you. And thanks for being on the Doctors Pharmacy Podcast. Thanks for listening today. If you love this podcast, please share it with your friends and family. Leave a comment on your own best practices on how you upgrade your health and subscribe wherever you get your podcasts and follow me on all social media channels at Dr.

Mark Hyman. And we'll see you next time on the doctor's pharmacy. Hey everybody, it's Dr. Hyman. Thanks for tuning into The Doctor's Pharmacy. I hope you're loving this podcast. It's one of my favorite things to do and introducing you all the experts that I know and I love and that I've learned so much from. And I want to tell you about something else I'm doing, which is called Mark's Picks. It's my weekly newsletter. And in it, I share my favorite stuff from foods to supplements, to gadgets, to tools, to enhance your health. It's all the cool stuff that I use and that my team uses to optimize and enhance our health. And I'd love you to sign up for the weekly newsletter. I'll only send it to

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