Dr. Eric Topol: Can AI Fix Our Health and Our Healthcare System?

Mark Hyman, MD

Dr. Mark Hyman is leading a health revolution—one focused on using food as medicine to support longevity, energy, mental clarity, happiness, and so much more. Mark Hyman, MD is a practicing family physician and an internationally recognized leader, speaker, educator, and advocate in the field of Functional Medicine. He is the founder and director of The UltraWellness Center, Founder and Senior Advisor for the Cleveland Clinic Center for Functional Medicine, a fifteen-time New York Times best-selling author, and Board President for Clinical Affairs for The Institute for Functional Medicine. He is the founder and chairman of the Food Fix Campaign, dedicated to transforming our food and agriculture system through policy.

He is a co-founder and the Chief Medical Officer of Function Health. He is the host of one of the leading health podcasts, The Dr. Hyman Show with 150+ million downloads. Dr. Hyman is a regular medical contributor to several television shows and networks, including CBS This Morning, Today, Good Morning America, The View, Fox, and CNN.

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Full Transcript

Automatically generated. Please forgive any typos or errors in the following transcript. It was generated by a third party and has not been subsequently reviewed by our team.

Dr. Mark Hyman: Coming up on this week's episode of the Doctor's Farmacy,

Dr. Eric Topol: It's going to be the biggest shakeup in the history of medicine. The question is how we adapt.

Dr. Mark Hyman: Welcome to the Doctor's Farmacy. 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, 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 1200 peer review 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.

Dr. Mark Hyman: His principle, scientific focus has been on individualized medicine using genomic digital and AI tools. He's authored three bestselling 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, topple is the principal investigator to two large NIH grants, the All of Us Research Program, which is really important. It supports precision medicine to where 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 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.

Dr. Mark Hyman: 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 topple is editor-in-chief of Medscape, which publishes the Substack Newsletter Ground Truths and Maintains a strong presence on social media and Twitter are 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. Topple 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 they didn't even know it was there.

Dr. Mark Hyman: 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 a reestablished a more humane approach to patient care. 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 Doctor's Farmacy 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 Doctor's Farmacy podcast.

Dr. Eric Topol: Well, thanks for having me, mark.

Dr. Mark Hyman: So your book that you wrote recently was the latest books called Deep Medicine or How Artificial Intelligence Can Make Healthcare Human Again, and everybody's hearing about AI and the South Altman and open AI and the dangers of AI and the controversies, the concerns, and 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 when you go to the doctor, you basically get your panel of 20 or 30 or 50 analytes, 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.

Dr. Mark Hyman: So I think 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. So 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 about 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?

Dr. Eric Topol: 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 a deep learning phase of ai and it really has been formidable. That is just about every type of skin you can imagine. But past 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 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 back in 2019 when I wrote deep medicine, it was about that phase of deep learning. But that's like

Dr. Mark Hyman: Ancient ancient history now, right? 2019

Dr. Eric Topol: In ai. Yeah, I know. It's amazing how quickly that has gone. Yeah, really Mark. But what's interesting is 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've alluded to that not just electronic health record, but our genome, our gut microbiome, our sensors, our environment, our immuno, the works and 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 major jump in a year ago with chat GPT and now of course the G PT four Gemini and future models G PT five sometime next year in fact. And that of course is getting us to that state where we could take all that data for a given patient or individual and be able to not only define what is so critical 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 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.

Dr. Mark Hyman: Yeah. When you talking about, 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 or metabolome or immuno, the transcriptome, proteome, maybe genome. It goes on and on. And then our electronic health records, our lab tests, our family history, unstructured text from our medical records, and also things that our 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.

Dr. Mark Hyman: All these things are going to be then informed by the whole Medline, 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 Omic revolution, the systems biology and medicine revolution, the biosensors and wearable revolution, and then the AI machine learning and big data analytic capacity that we have. And so those five basic trends are all converging and 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 super sub sub subspecialist on X, y, or Z topic, but you don't understand how it all connects and interacts.

Dr. Mark Hyman: And so the first time with ai, it seems like we're able to do that. So how do you see this unfolding and how is this kind of happening and where are we going? 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 of people's health.

Dr. Eric Topol: 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 I've been calling keyboard liberation. Yeah,

Dr. Mark Hyman: Thank God. I heard that you say that. I'm like, hallelujah. Every doctor is stuck on their keyboard looking at the computer instead of looking at the patient. And so being free of that is so huge.

Dr. Eric Topol: It's hated mutually by doctors and nurses and patients. I mean everything that people love to hate because it's destroyed that bond, that human bond. 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 and 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 in terms of educational level or language or 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 auto file, listen to it again, and 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, they orders for new tests and labs and return appointments, prescriptions, billing.

Dr. Eric Topol: 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? The AI picks that up, gets it back to the physician. All these things are now automated, so that will in itself be welcome instead of the things that all clinicians want to hate. This is I think, something that will be widely embraced. And there's no, as 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.

Dr. Mark Hyman: Do you have that in your office yet? Do you have that in your office yet?

Dr. Eric Topol: 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 a Microsoft Nuance, but the company that I've advised is a bridge health, which would just derive 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.

Dr. Mark Hyman: Sam, I want to get one right away from my practice.

Dr. Eric Topol: I mean, I 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. It's just one thing that's going to be remarkably different

Dr. Mark Hyman: And that helps us do care better, but it doesn't change what we're doing. In other words, 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 on understanding of how to be precise in our diagnosis of patients. And that's going to uplevel medicine for sure.

Dr. Eric Topol: Yeah. Can I just Yeah, go one thing. Yeah. The retinal image is something that is extraordinary. So before we just pass over that.

Dr. Mark Hyman: Yeah, yeah,

Dr. Eric Topol: No, it's very cool. 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 course, hepatobilliary tract kidney disease, cardiac risk, risk of 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 that course that extends to cardiograms and Chest x-Ray, each of them, there's all this stuff that the AI can see, if you will, that humans will never see it. Never see.

Dr. Mark Hyman: It's even better than humans, right?

Dr. Eric Topol: Yeah. I mean this is why when I interviewed Jeff Hinton recently for the podcast, I do ground truths. He said 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.

Dr. Mark Hyman: I agree. I mean, I remember in medical school you had the ophthalmoscope and you had to look in someone's eye and 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. And if you're 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 does it actually seeing and looking at, for example, for Alzheimer's,

Dr. Eric Topol: Well, 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 and the AI got to write 97% of the time. And the answer is we don't really know. That is there's explainability work to define the 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 a pat biliary 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 this transformer model era. So

Dr. Mark Hyman: Can you 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? I don't think most people understand what that is.

Dr. Eric Topol: So what was the phase of AI that lit up the world? Jeff Hint and his colleagues like Jan Koon 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 truth. And so they put it what they knew was the actual image interpretation and train with tens of hundreds of thousands of these images so that the machine could see stop.

Dr. Mark Hyman: So this is the knowledge base or expert informed ai, right?

Dr. Eric Topol: Yeah. So that really was a 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 pre-print 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, the Wikipedia, a hundred thousand books, 200,000 books. So that's what the transformer model, large language model, generative AI year that we're in now, it didn't start when chat GPT was released last year, but it actually was in incubation. It was being pursued about six years now, but it is now blossomed and we basically have two big types of AI now. The old, if you will, the old and the new.

Dr. Mark Hyman: Yeah, I mean it just seems it's going to accelerate the pace of medical discovery if a simple retinol scan can pick up things that we didn't even know we were missing. We didn't even know. We didn't know they were unknown unknowns as Donald Rum said. Yeah, exactly. 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 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 for you to quantum view to even beyond that, it's like we're kind of in that era of biology. We basically have a profound revolution that's going to upend medicine. And I had 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 silos, specialties, how do you see that shifting medicine, medical education, medical practice, reimbursement? I mean this is a massive shift.

Dr. Eric Topol: Well, it is seismic. 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 is going to be easy, but it's going to be the biggest shakeup in the history of medicine. The question is how we adapt 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 that it basically is the bypass to what back medicine. But just like that problem, 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 is it, that this is going to lead to better patient outcomes, better everything. And there's always going to be some risk of course, when there's never going to be total positive side of the story. But except for the gastroenterologists who have done 33 randomized trials of colonoscopy with machine vision and a few other randomized trials and radiology that have been quite impressive, particularly mammography, there hasn't been much compelling evidence so far.

Dr. Mark Hyman: Yeah, it's true. It's true. But on the other hand, you look at the amount of deaths are caused by medical practice, probably a third or fourth leading cause of death are 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, what about what 40,000 people in America die from car accidents every year? What if that was reduced to 10,000? But that's a dramatic drop, but still you're going to have some people dying from a self-driving card, and are we willing to accept that? 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.

Dr. Eric Topol: Well, 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 the Johns Hopkins study that was published earlier this year in the British Medical Journal 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 low US possible, but that hasn't been the way medicine has worked. The medical error of diagnosis is something that's got to be confronted.

Dr. Mark Hyman: Yeah, I think you're right. I think when I think about what actually medicine is today, 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 that went to medical school. I went to medical school in 1983, I went to medical school. I've tried to keep up and I've had my own narrow experience, and that patient is relying on me to create all these associations, pattern recognition, be able to sift through all the medical literature, whatever it was, the 9 million published articles on PubMed and make sense of what's going on with them. It's kind of embarrassing. I feel like I'm kind basically groping around in the dark and I still obviously help people and obviously you do it, 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.

Dr. Mark Hyman: So how do you see the AI decision support process happening unfolding? I think this is where I'm most excited about,

Dr. Eric Topol: 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 internists. And then when you gave the internists a Google search, it didn't make that much difference. And even when you gave the internists then being able to go to GPT, well, it was a med palm two fine tune.

Dr. Eric Topol: We don't know exactly what it is, but it's some large language model that 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, and especially in these challenging complex, it could be rare conditions, just very difficult cases. What it does is it may bring to mind some conditions that didn't pop into our head. And so basically 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 to her son for her son put symptoms of after seeing 17 doctors for three years for a boy who is just debilitated and growth arrest and then gets the diagnosis of OC cold spina bifida herself through chat TPT. I mean this is happening now. You can't hold back patients from using now they too,

Dr. Mark Hyman: Right?

Dr. Eric Topol: So 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.

Dr. Mark Hyman: Yeah. 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. As we now know, diseases aren't homogenous, and our current diagnostic model is based on symptoms and pathology and geography. 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 pheno that we just described. And as we start to sort of ingest the pheno into this AI monster, whatever you call it, or AI genius it, 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 Alzheimer's isn't uniform, diabetes isn't uniform. If you have breast cancer, we know this.

Dr. Mark Hyman: If you have, I mean years ago I read a paper and I think a jamer journal was like 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 true, just true for cancer, it's true for everything. And whether it's rheumatoid arthritis or whether it's irritable 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 we're really good at this diagnostic differential diagnosis process. That's where 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, breaking that old paradigm and helping us create a more individualized approach to different diseases?

Dr. Eric Topol: I'm really glad you brought this up because that's the reductionist. 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, things like type two diabetes, just type two, there's but like 20 subtypes of type two and on and on too. So we will have this kind of pinpoint precision and accuracy, which will help promote very specific individualized treatment. And that can also extend to what would be the best optimal diet for any given person because we respond to food remarkably differently. Everything we do, we are unique. Even two identical twins are unique. They have very different epigenomes. And 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 preventing, diagnosing, treating, getting better outcomes and promoting health span.

Dr. Mark Hyman: Yeah, I think that'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 or whatever, which 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 pH, 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 whether 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 that 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?

Dr. Eric Topol: 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 this era so extraordinary that we couldn't really have this personalized preventive medicine. And frankly, mark preventive medicine has been a fantasy throughout our careers. We want to prevent primary prevention of illness is almost unheard of, but secondary prevention and once you've had a heart attack, prevent a 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, 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.

Dr. Eric Topol: We are going to have ways to prevent cancer, like the earliest sign to rev up the immune system to squash it or to go into high mode surveillance to get frequent 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, we'll 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, I mean heart failure, no, there's 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.

Dr. Mark Hyman: Yeah, I think it's true. And I think one of the things from my perspective is this whole idea of, I wrote a book called Ultra Prevention 20 years ago. It essentially was about this. It's like 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 colonoscopy or whatever we're doing, and we're entering here 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.

Dr. Mark Hyman: And for me, it's this best of worst at 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 advanced in medicine. So how do we reconcile these two? It's like how do we use one to solve the other? I feel for me, this is the biggest problem that no one's really addressing us. We have 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.

Dr. Eric Topol: Well, yeah. I mean, isn't one simple thing. Of course, there's lots of moving parts here. On the one hand, 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 Glip one family drugs. Yeah,

Dr. Mark Hyman: Like OEM we're

Dr. Eric Topol: Talking, yeah, when Mandura and then the triple receptors that will follow, but we're only at the beginning of that. And who knows how many people ultimately will have their weight issues addressed. And of course the toll of obesity on every system in the body is profound. Now, at the same time, we ignore our environment. Yesterday, my friend Sid Mukherjee had a great article on the carcinogens, air pollution, and I actually think that ultra processed foods and a lot of other things that we take in our food stuff and beverages is potentially a liability for bad health cancer, cardiovascular risk. We don't pay attention to

Dr. Mark Hyman: It. I'd remove the word potentially. Yeah. I think the data is so clear on this ultra process food link.

Dr. Eric Topol: Actually, I shouldn't have used that word. I'm glad we're in a accord. Yeah, no, not potentially. The data is overwhelming, but nobody does anything about it. What are we doing to get rid of the carcinogens and the things that are accelerating major chronic diseases like heart disease and cancer and neurodegenerative diseases? So we've identified them, but we do nothing about it. And you remember, mark, how long did it take to get trans fat to be outlawed?

Dr. Mark Hyman: But they're not even, I mean, even the government has ruled them as non grass generally. They 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, is lobbying efforts.

Dr. Eric Topol: So this is amazing. So we know some of the culprits, but we have no teeth. We do nothing about it. Europe, they actually have done better on this than the us so this is really disheartening.

Dr. Mark Hyman: 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 Truth. I love this. Ground truth is like what are the fundamental laws of nature? Is sort of how I think about it. What is 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, our sleep stress, environmental toxins, things that we actually can modify that doctors actually pay very little attention to, right?

Dr. Eric Topol: Yeah. Well just look at, you're well aware of this. How come people in their twenties now are getting colon cancer? How come people in their thirties who never smoked are developing lung cancer, breast cancer? It isn't their genes. 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 is like a blind eye to this stuff.

Dr. Mark Hyman: It's true. It's shocking to me that Fatima Sanford from Harvard, who's a professor, is now in the US 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 where were all the obese people? When you and I were born, there was five 10%. Now it's 42% a hundred years ago, it was nothing. And it's kind of frightening to see that level of scientists who's now on a federal guidelines committee coming up with this concept and not talking about the real issue. And she's got funding from Ozempic, and it's just like, oh my God, it's so corrupt. It reminds me and kind of where we are now, in a way, I think it is a little pejorative for doctors, but I think it kind of reminds me of Voltaire saying where he said, doctors are men who prescribe medicines of which they know little to cure diseases of which they know less in human beings of whom they know nothing.

Dr. Mark Hyman: And I think when you're going to look back maybe in five or 10 years and go, what the heck were we doing? What were we thinking? I feel like it's such an, I'm just sort of sitting on the edge of my seat. It's such an exciting time. And I think 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?

Dr. Eric Topol: 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 hospitals at home where 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 I've watched this go down the tubes. I mean, back when I finished med school and started training, postgraduate training in the early eighties, the relationship with a doctor and patient was a precious thing. I mean, you knew the patient knew that you were there for them, that when you met with them, you were there, you were

Dr. Mark Hyman: Present, you were present,

Dr. Eric Topol: And 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, there are some exceptions, but there's not the time. And that's why I really think this gift of time that AI can bring us, whereby we give more autonomy to patients. So that decompresses the time of clinicians, I think this would be the most exciting thing to derive from the AI era. We have 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.

Dr. Mark Hyman: Yeah, I think it's so important. And it also speaks to the need to help those who are not having access to healthcare and medicine who don't have experts in their area. I remember when I was a family doctor in our Idaho, in the middle of nowhere in a logging town, and we were it, there was 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 I a premature birth. I got put in this umbilical catheter and I would be coached through it. But I was a rural doctor back then and there now people globally were facing a doctor shortage and nursing shortage problems with accessing healthcare. And I think these technologies will actually help people in those areas and some of these health disparities and skill at medicine in a way that I think we haven't seen before.

Dr. Eric Topol: I really agree with that. I mean, we've got to do something to address the inequities. And they include not just socioeconomic, but rural versus urban like you're getting at, and 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 just 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,

Dr. Mark Hyman: 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 copilot is going to be possible by ingesting this large genomic data. All the stuff that I said at the beginning, all your 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 exercise better simple practices you can do. And I've found that as a functional medicine doctor, most of my patients can get better from things without me if they just had the roadmap and could follow it and do the self-care practices.

Dr. Mark Hyman: Because most conditions that are chronic illness often can be solved through really simple basic lifestyle or other changes. And I 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 can now I wear 'em an ora ring or a watch. You can get your biosensor data, but what about what's under the hood under you? And so now I think we're going to co-founded this company called Functional Health, which is designed to be an AI powered personal health copilot where all this data's going to be ingested and you'll be guiding on what to do for self-care and then when to go get medical care and what kind of medical care you might need.

Dr. Mark Hyman: 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's 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.

Dr. Eric Topol: Yeah, no, I think this is really interesting what you're bringing up. So firstly, there are now 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. So they, some of them have done randomized trials to show they're really helping people. So 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 the cover last week.

Dr. Mark Hyman: Like aging clocks, you mean? Or

Dr. Eric Topol: Yeah, aging clocks. So you know how you can get so-called horvath or epigenetic clock to what is your biological age, and hopefully it's similar or better than your chronological age.

Dr. Mark Hyman: Well, mine is 43 and I'm 64, so I'm going with that.

Dr. Eric Topol: Oh, okay. There you go. Good for you. Wow. Wow. Anyway, isn't that helpful? Compared it, I think with organ specific age. So what they discovered, which is using ai,

Dr. Mark Hyman: Yeah, my back is about 110, but

Dr. Eric Topol: 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 and proteins that can be assay 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 we didn't have before to add to the mix. So I do envision this virtual coaching with periodic, whether it's retinal assessment or organ specific clocks or you name it. 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 organ clocks. And I think that to me was really one of many important in a chain of recent discoveries.

Dr. Mark Hyman: Well, that speaks to the real issue here, which is what are we looking at as doctors? We're looking at basically things with a magnifying glass, not a microscope. And now we have an electron microscope to even look deeper. And I think there's tens and tens of thousands of molecules and proteins and metabolites and 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. If you do an extensive panel of tests, you might even get 50, a hundred blood tests. But that's kind of minuscule when it comes to what's really going on. And so what you're talking about really is 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 just the last few minutes we have, I'd love to have you pontificate on how we are going to reimagine research this era of AI enabled medicine.

Dr. Eric Topol: 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, BRCA two mutation recently published in 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, never goes into patients, 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, I think you recognize there's this paternalism in medicine and the patients don't tell 'em about their genomic data because they won't be able to handle the truth. It's

Dr. Mark Hyman: A little patronizing. It's a little patronizing. Yeah.

Dr. Eric Topol: It's their data if they want to get their data and they want to know what caveats there are. And 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,

Dr. Mark Hyman: 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 swe, who sort of figured out that we should wash our hands before child attending to birth. And he saw that the midwives patients were not dying of childbirth fever, and all the doctor's patients were, and he said, 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. I know it's kind of like that right now where the advances are happening so rapidly in medical science. And my daughter's in medical school now, and I'm like, just so embarrassed. Oh, great. I said, Rachel, are you learning about the microbiome? Did you learn about insulin resistance? No. Did you learn about nutrition? Well, we learned about amino acids and fatty acids. I'm like, whatcha going to tell your patient to eat?

Dr. Mark Hyman: 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 am so excited that you've been thinking about this leading this work. You, you've done so much in your career. That's advanced science thinking so much. And it really makes me very inspired to know people like you who are at the peak of healthcare 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.

Dr. Eric Topol: Oh, thank you. Thanks, mark. That means a lot. And healing, caring, those are the key. We got to get to that and do it better and leverage what we can out of i, AI and machines to help us get there.

Dr. Mark Hyman: What was that quote? The secret of caring for the patient.

Dr. Eric Topol: The care of the patient is caring for the patient. Francis Peabody, 1927. My favorite quote, actually.

Dr. Mark Hyman: Yeah, that's right. That's exactly true. And that'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 on it.

Dr. Eric Topol: Yeah, it's coming. It's going to happen fast, you'll

Dr. Mark Hyman: See. Alright, any final thoughts, Eric, on where we're headed and what you're inspired about next?

Dr. Eric Topol: 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 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 this is really a unique time to look forward to. I hope we'll get there. The sooner the better.

Dr. Mark Hyman: Well, I think both of us obviously take care of our health and our focus on our health. So hopefully we're going to extend our life long enough. We'll be alive when all this happens.

Dr. Eric Topol: I hope so.

Dr. Mark Hyman: 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 Doctor's Farmacy 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 Farmacy. This podcast is separate from my clinical practice at the Ultra Wellness Center, my work at Cleveland Clinic and Function Health, where I'm the Chief Medical Officer. This podcast represents my opinions and my guest opinions. Neither myself nor the podcast endorse with the views or statements of my guests. This podcast is for educational purposes only. It's not a substitute for professional care by a doctor or other qualified medical professional. This podcast is provided on the understanding that it does not constitute medical or other professional advice or services. If you're looking for help in your journey, seek out a qualified medical practitioner. Now, if you're looking for a functional medicine practitioner, you can visit ifm.org and search their find a practitioner database. It's important that you have someone in your corner who is trained, who's a licensed healthcare practitioner, and can help you make changes, especially when it comes to your health.