The Dr. Hyman Show - Inside the New Era of Precision Medicine: Where AI and Human Insight Unite

Episode Date: August 11, 2025

Medicine stands at the threshold of a new era, where artificial intelligence and systems biology are working hand in hand to make care more personal, predictive, and precise than ever before. AI is already improving diagnostic accuracy, automating administrative tasks, and uncovering patterns in data—like retinal scans or genomics—that humans often miss. Rather than replacing doctors, AI enhances their ability to deliver more informed, precise, and efficient care. At the same time, individuals are gaining tools—from at-home diagnostics to wearable biosensors—that empower them to track and optimize their own health. This shift marks a move from reactive, disease-centered care to a proactive, data-driven model of scientific wellness. In this episode, I talk with Dr. Eric Topol, Dr. Nathan Price, Dr. Leroy Hood, Dr. Vijay Pande, and Daisy Wolf about how artificial intelligence, personalized data, and wearable technology are converging to radically transform medicine. Dr. Eric Topol is Executive Vice President of Scripps Research and founder/director of its Translational Institute, recognized as one of the top 10 most cited researchers in medicine with over 1,300 publications. A cardiologist and author of several bestselling books on the future of medicine, he leads major NIH grants in precision medicine and shares cutting-edge biomedical insights through his Ground Truths newsletter and podcast. Dr. Nathan Price is Chief Scientific Officer at Thorne HealthTech, author of The Age of Scientific Wellness, and a National Academy of Medicine Emerging Leader. He also serves on the Board on Life Sciences for the National Academies and is Affiliate Faculty in Bioengineering and Computer Science at the University of Washington. Dr. Leroy Hood is CEO and founder of Phenome Health, leading the Human Phenome Initiative to sequence and track the health of one million people over 10 years. A pioneer in systems biology and co-founder of 17 biotech companies, he is a recipient of the Lasker Prize, Kyoto Prize, and National Medal of Science. Dr. Vijay Pande is a General Partner at Andreessen Horowitz and founder of a16z Bio + Health, managing over $3 billion in life sciences and healthcare investments at the intersection of biology and AI. An Adjunct Professor at Stanford, he is known for his work in computational science, earning honors like the DeLano Prize and a Guinness World Record for Folding@Home. Daisy Wolf is an investing partner at Andreessen Horowitz, specializing in healthcare AI, consumer health, and healthcare-fintech innovation. She previously worked at Meta and in various startups, holds a JD from Yale Law, an MBA from Stanford, and a BA from Yale, and is based in New York City. This episode is brought to you by BIOptimizers. Head to bioptimizers.com/hyman and use code HYMAN10 to save 10%. Full-length episodes can be found here: Can AI Fix Our Health and Our Healthcare System? The Next Revolution In Medicine: Scientific Wellness, AI And Disease Reversal The Future of Healthcare: The Role of AI and Technology

Coming up on this episode of the Dr. Hyman Show. The thing about replacing doctors, the line that I really like, I think it's Eric Philpels, which is AI won't replace doctors, but doctors who use AI will replace doctors who don't. And I think that is a really good way to put it because it is a tool. If you're living on caffeine, constantly tired, and feel like your stress is stuck on high, there's a good chance you're low in magnesium. Magnesium is critical for turning food into energy, calming your nervous system, helping you sleep and regulating your mood.

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Magnesium breakthrough covers them all for deeper sleep, better focus, less tension, and real support during stressful times. If your body's feeling off, this might be the missing link. Try it risk-free at bioptimizers.com slash hymen and get 15 printed off your order. Before we jump into today's episode, I want to share a few ways you can go deeper on your health journey. While I wish I could work with everyone one-on-one, there just isn't enough time in the day, so I've built several tools to help you take control of your health. If you're looking for guidance, education, and community, check out my private membership, the Hyman Hive for live Q&A's exclusive content and direct connection.

For real-time lab testing and personalized insights into your biology, visit Function Health. You can also explore my curated doctor-trusted supplements and health products at Dr.hyman.com. And if you prefer to listen without any breaks, don't forget you can enjoy every episode of this podcast ad-free with Hyman Plus. Just open Apple podcasts and tap try free to start your seven-day free trial. 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, electric cardiograms, the retina, as you mentioned, skin lesions, they could be interpreting. it 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, you know, back in 2019, when I wrote D-Medicine, it was about that phase of

deep learning but that's like ancient history now right 2019 yeah no it's it's amazing how quickly that has gone yeah really mark but what interesting is you know i wrote in the book uh 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 uh you know you've alluded to that not just electronic health record but our genome um you know, our gut microbiome, our sensors, our environment, our immunome, the works, right? And in 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.

Now, of course, the GPT4, Gemini, and future models, GPD5, 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, you know, there has no one has actually done multiple layers. They've done electronic health records and a geno, 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're 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, or microbiome, or metabolome, or immunome, 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 are air pollution sensors we could be wearing. I just got one of those that someone sent me to try to wear it in my air pollution, environmental stressors. All these things are going to be then informed by the whole, you know, medline. I, 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 so an 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 Omex 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 in a way that, 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, you know, I feel like, you know, I've been trying to, with my little brain, put my head around all these, these immense complexity of human

biology, which, you know, we've managed to navigate through this reductionist model of medicine and science into siloed specialties where you're a super 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 do that. So how do you see this unfolding? And how is this kind of happening? And where are we going? Because I feel I feel like I feel like I'm sitting on the edge of my seat. And right now, I feel like, you know, 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 people's help. 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 the practical, which is, you know, I've been calling keyboard liberation. 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 so being free of that is so huge. It's hated mutually by doctors and nurses and patients. I mean, everything that people love to hate because it's destroyed that bond, a human-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 than the ones that are pecked along. And what's great is once you have that note digitize and it's got all the juice in it, two big things happen. One is that, of course, you could put it 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 auto 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, because instead of having to peck through all the stuff,

the orders for new tests and labs and return appointments, prescriptions, billing, pre-authorization, 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 there's no, you know, You know, as you know very well, Mark, there's a lot of concerns about confabulation, hallucination, but that doesn't apply here. I mean, this is not, 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. I want to get one right away from my practice. Yeah, 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.

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 than our diagnosis of patients. and that's going to up-level medicine for sure.

But let me, can I just time in one thing? Yeah. Because the retinal image is something that is extraordinary. So before we just pass over that. Yeah, yeah, that's very cool. You know, I just want to point out that, you know, the original task was to see if the AI could interpret the image as well as a clinician. But what was an envision is that the AI could see things that, you know,

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. We will. We will. We will, 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 it. So 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 truce, 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, okay, you learn about AV-knicking and

high blood pressure and diabetic retinopathy and macular generation. You can 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. and the AI got to write 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.

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 can you just stop there for a second? You're talking about deep learning transformer model. Can you just explain the sort of shift in what you're thinking? Because I don't think most people understand what that is. Right. So what was the phase of AI that led up the world that Jeff Hinn and his colleagues like Jan Lacoon 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 train with tens of hundreds of thousands of these images so that the machine could see stop. So this is an acknowledged 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 called 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 chat GPT 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 retinal 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 Rums on set. 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, quantum view to even, you know, beyond that. It's like we're kind of in that era of biology where we're basically have a profound revolution that's going to upend medicine. And I'd love to hear a perspective on as we sort of enter that era and we start learning these things and understand.

understand the body as a network, understand the body is 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. As medicine, as you know, doesn't change easily. And then you get, you know, throw in all these other practical matters like, you know, reimbursement and education, regulatory, trust, implementation. I mean, there's a long list here of challenges. So, you know, this is going to be easy, but it's going to be, you know, the biggest shakeup in history of medicine.

The question is 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, you know, 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's, it basically is a 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 is it, you know, that this is going to lead to better patient outcomes, better, you know, better everything. And there's always going to be some risk, of course, when there's never going to be, you know, 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 and 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, you know, on the other hand, you look at, you know, the amount of deaths are caused by medical practice, probably a third or fourth leading cause of death or, you know, complications or reactions or drugs or medical errors. It's huge. And I was listening to Elon Musk talking 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 self-driving car and are we're willing to accept that. You know, so I think, I think that's really a point where we have to kind of understand

the value proposition and understand that, you know, there is some risk. But the upside in terms of reducing our health care costs, the burden on our health care system is going to be profound. You don't need more caffeine or another stress supplement. You might just need more magnesium. Magnesium supports sleep, mood, energy, and focus, but most of us are missing it. That's why I recommend magnesium breakthrough by bioptimizers. It combines seven forms of magnesium.

For results, you can feel in your mood, sleep, focus, and more. Try it now for 15% off at bioptimizers.com slash hymen and feel the difference. One of the things that we've done recently is to get into digital twins. And so a digital twin is a representation of your body's physiology. And we've done this first for brain health. And so what we can actually do in this case is, and we're going to release, say, a test on this, a product based on this next year. But basically what you can do is you can monitor for a number of these blood measures, your genetics, cognitive assessments, and so forth. And you can then run a simulation based on your particular biology.

and it's based around the, you know, understanding from a physiologic and molecular level what's driving brain health. And you can actually forecast the likely amount of time that you have with a healthy brain given your current state. More importantly, you can go to personalized recommendations for different kinds of things that people can do, some of which are, you know, exercise to keep your oxygenation in your brain high. you can get into things like Phosphatidylcholine. Turns out that that becomes rate limiting under low oxygen conditions.

Late as people are developing dementia, hugely important. Vitamin D, very simple one. We could talk a lot more about that one. Turns out to be very important. There's many, many of these. But the point is that what you can actually do with the digital twins

is you can get a representation of a person's individual risk profile and then tailor the precise recommendations. These recommendations are very different person to person. Once you get to four recommendations, only 1% of people actually benefit from what's the best thing, the best for, and the population. We just did those simulations.

So it's very interesting when you do that. And so you get this intense personalization, and you can get into the physiology, and you can start to make sense of this. Because you have to take the complexity of all these measures. You can't place that on a person. You have to put that into the algorithm. rhythms and deliver back simple, actionable information.

And then the other side of the coin, which I'll just mention here briefly, is the chat GPT and all these things that we have shocked the world over the last year. The ability now to deliver personalized insights that give you a lot of context and that you can have a back and forth with and you can get access to a dialogue even with what your digital twin is saying or what you're learning about your body or you like these the capability for us to develop personalization on that front is just radically better than any of us thought it was going to be a couple years ago and so those things together are really pushing us into this new world of where we're going to be able to harness so much more of this complexity than we could have even

thought about before i mean i mean this chat gbt there like now for example i put in all my symptoms, I enter in all my lab data and I hit, you know, tell me what's wrong and what to do about it. Would it give me anything useful at this point or is it still far off? So I've played with this a lot, so maybe I'll jump in on that. But it's pretty much what I do in my free time. I don't do anything else. You're up a contract. You put it in all your symptoms. My stomach or I got a head pain. Yeah. It's partially there. If you use like earlier versions, like the GPT 3.5, for example, You'll get lots of hallucinations. It's sometimes useful, sometimes not.

GPT4 is pretty good, except, anyway, there's this weird trend. It's not as good as it used to be. And there's a lot of chatter around that on it. It doesn't let you go as deep as it used to. I don't know if it's legal or they're not really talking about it. Yeah, they put guardrails on it, yeah. They put guardrails and various kinds on it and so forth. But as long as your, if your question is reasonably well dealt with in available text

that it's generating from, it can be quite good. And I've had, and I've used it, you know, not just on medical issues, but, you know, explain statistical analysis of this kind of data or something like that. And it's, it actually gives back really reasonable kinds of information. Now, it's not fully to where it wants to. Oh, and I did see a survey. Maybe you saw this as well. They pulled doctors, and apparently 60% of doctors are using GPT today right now in the background on things that they do. So I said, if you saw that survey.

No, actually, but it was interesting. Not totally ready for prime time, but just to say that. Yeah, go ahead. Well, no, I was at this big metal conference in Lake Nona, and they had this guy from Microsoft with, I think, Prometheus, which was kind of a new version of, like, Chad GBT that was, like, you know, four doctors. And they had a case report that they were sharing and they were entering in this case study. And it got it totally wrong.

And I guessed it immediately. Like, I wouldn't guess it. I just knew what it was because I listened to the story. But, you know, it was basically a patient who had. you know, frequent urination, fever, chills, you know, had had, I think maybe had had a history of rheumatoid or strep long ago or something like that. Or had a murmur, maybe had a murmur as a sort of part of the exam. You know, it was just a murmur. And I'm like, oh, this guy has endocarditis. This guy has bacterial endocarditis. And the chat, the Prometheus thing said, oh, he's got a, you know, kidney

infection. And I'm like, no, he's a kidney infection. And it was wrong. And it was like, in front of like 500 people so you know i kind of wonder but i do think that that um you know the things are changing so as you as you've gotten into sort of looking at the these sort of enormous amounts of data through the pheno typing of people you know when that goes into these machine learning AI models like you know where is the next step in this in medicine is are we all kind of kind of moving towards this, our doctor's going to become in some ways obsolete, or they think it's going to be helping to kind of, you know, implement some of the decision support that these tools give. Because personally, I would love to be able to put all the data for my patients in and

instead of spending hours and hours muddling over and thinking about it, trying to remember every study I ever read and what to do in my medical school training, like, this is going to give me kind of a roadmap to start with and then implement it. How far are we away from that? Well, I'll make a couple of comments. I think a really important thing about these large language models, which is what GPT and the other things we've talked about are, is that they have to be educated properly. So if you take a large language model and you expose it to the Internet,

you expose it to the conspiracy theories and the lying and all of those other things, you have an enormous susceptibility in that device. And my argument is for health, we ought to have a GPT that has only been educated with biomedical data. And we're actually collaborating with a group that has one of those. And what our hope is, is will, and part of the education has been to put PubMed into the device. which gives you an enormous amount of data. Now some is right and some is wrong. You'll still have to make judgments.

But what we plan to do is we have access, for example, to Google's knowledge graph. And this is a graph that connected roughly 50 different features from the literature. So it's assembled from the PubMed literature all of the relationships between genes and proteins and diseases and drugs and on and on. PubMed for those listening is just the entire body of peer-reviewed, published medical research. Biological information.

Yeah, it's a lot, it's millions of millions of studies. Well, this knowledge graph has 50 million nodes and 850 million edges, which means an enormous. number of relationships. So we're going to put this knowledge graph in this medically educated GTP. And we're going to put in, we're building now a knowledge graph for the kidney. We'd certainly like to put in the knowledge graph for brain health. All of the knowledge graphs and digital twins that we have should go into educating this thing. And then my hope is the following, we'll be able to take the data, genome, and phenome from each individual enormously more complicated than what we did in Aarvale, maybe 10 times as much data as we had initially, and put it in there and ask it to generate from tens of thousands of actionable possibilities, the ordered priority of action.

possible possibilities that you as an individual can use to optimize your health or avoid disease or whatever. And what the AI will actually do is send this information to a doctor, and there'll be two things the information will have to do. One, clearly explain the actionable possibility in what the doctor and the patient will be expected to do. But two, it's to give the physician the medical evidence for this actionable possibility to assure him or her it's bona fide. And the dramatic result of this is you will be able to take a family practitioner and make him a domain expert in virtually every field of medicine. It gives you this global reach that you were talking about and the capacity to handle virtually anything. And that democratizes medicine in an incredible way. And I'll argue, well, never, ever get rid of the physician because they're, in the end, still an integrated factor that were a long ways from being able to replicate and so forth.

but he will have the tools to become a world expert in every field of medicine. Really quite a remarkable promise for the future. And what it promises for patients, that is the optimization of this wellness and prevention, Nathan and I have talked about, I think it's really dramatic. So how far away from this are we? So I think we'll begin.

to see the effects of this within the next year or so as these things get. I mean, we won't have them in the full glory for, you know, who knows. Maybe 10 years is way too long to say because, look, I mean, that 60% of the doctors would use a tool like this, I would have said there's no way in the world that that conservative group of people would ever go into AI like this. And yet... So they're putting their patient's history in there and saying, hey, what's wrong? Is that what they're doing?

Yeah. That's amazing. I know, we should probably not over us. It means they use it to some degree. Because the thing about like replacing doctors, the line that I really like, I think it's Eric Koppels, which is, you know, AI won't replace doctors, but doctors who use AI will replace doctors who don't.

And I think that is a really good way to put it, because it is a tool. And I think it's like today, it's already a super useful tool. Like if you're trying to remember something or if you want to delve into the literature, it's so, you know, you can, and especially with these particular GPTs that are based around PubMed and things like that, they're already an assist, right? So it's just already a function of how strongly that assist can be made. And I think the doctor is still going to be the quarterback, but your ability to blow.

lock and tackle and just solve lots of issues with the AIs is incredible. It's not just the LLMs. I mean, one of the really biggest uses that's, you know, straightforward right off the bat is getting rid of as many medical errors as possible, right? Because a doctor who's tired, it's easy to, you've got a long, complicated name, and there's two of them that look almost exactly the same. It's pretty easy to accidentally check the wrong box. But if the AI actually knows, well, you said your patient has diabetes, and that's a drug,

Did you actually mean this drug for multiple sclerosis? And that's already happening today, right? Hospital systems have saved millions of lives already by just implementing some of those really simple things, the kind of mistake that's easy to make as a human and a computer won't make. Now, vice versa, computers will make the kind of, you know, and AIs will make errors that a human never would because they don't understand causality. They don't understand the context.

They don't, you know, there's all kinds of stuff, like the case study that you got right that the AI didn't, like, there's things that it doesn't know. So a hybrid, or what we call Centaur AI in the book, a hybrid approach really makes a lot of sense so you can cover your bases because those two kinds of intelligence, human intelligence, and AI actually operate quite differently, and the kind of errors you make are very different. So combining them is powerful.

What you're talking about is definitely going to help transform the expertise of physicians and allow them to practice medicine that's more up-to-date that reflects the scientific literature that is based on understanding a wide network of biological factors that they haven't been able to consider before. And that's going to be fantastic. But the truth is that wellness, health does not happen in a doctor's office, right? And so 80 to 90 percent of the things that determine your health actually don't require a doctor and are things that you can learn about yourself and fix without a doctor's help.

And so in a way, this is also going to help, I think, disintermediate people from a health care system and from doctors because we don't really have a health care system. We have a sick care system. And so what you're talking about is actually a new kind of health care system where people are going to be empowered with their own health data guided by, you know, these big, dense data clouds of their own biological information from all their omics to their blood panels to things we don't even measure now that we're going to measure to their wearables and biometrics. I mean, I have a garment watch. I mean, I know everything about myself. My pulse socks, my heart reverability, how much I slavit, how much sleep, how much light sleep, but my training this is, you know, like how much time I need to recover. I mean, it's pretty impressive.

And all that is just sitting out there ready to be kind of harvested and used. And so individuals, I think, are in this moment where they can become more empowered to be the actors in determining their own degree of wellness and health and then know when to go to the doctor, like, oh, well, gee, you know, your creatinine's like, five, you better get your ass over to the nephrologist tomorrow, so that's going to for sure be still there. But a lot of the stuff that actually requires a physician isn't really needed. It's really diet, lifestyle, you know, behavioral changes, supplements, in other practices that they have access to. So how do you see this kind of being a tool that the individuals and patients and

consumers can use in a way that is really going to disrupt health care? You know, Mark, I think you made a really excellent point, and that is the important. of education for the consumer, if you will. And we're doing a number of things in that regard. For example, this past year, an educational team at the Institute for Systems Biology that I initiated 20 years ago to deal with K-12 science education problems has put together a four-module one-year course based. on two chapters, several of us wrote in a systems biology and systems medicine book,

one on systems medicine, one on P4 healthcare. And the essence of this module is to give them the picture that is portrayed in our book of what health care is going to be in the future and to clearly explain the responsibilities they'll have for their own education, and it makes very strongly the point, the core of your health is going to be diet, exercise, sleep, stress, et cetera. And these are things you can do about it, and these are tools and devices you can use to measure it.

And oh, by the way, there is this more sophisticated medicine of assayne, blood and your gut microbiome that can tell us and by the time students will get done with that year course, I'll guarantee they'll know more about what I think, what we think the future of medicine is than 95% of the physicians out there. I mean, this revolution in transforming healthcare from a disease orientation to an orientation of wellness and prevention, I I can't stress how important that's going to be in doing two things. One, improving the quality of health for every single individual that practices even partially. And two, it's going to lead to enormous cost savings in the health care system by avoiding what costs 86% of our health care dollars today, namely chronic diseases.

And Mark, I'd love to kind of weigh on that question as well that you ask because I think it's such an important thing because you're exactly right. Because the more and more of what we can put under health care, especially if we start talking about wellness care, right? We like to say scientific wellness should be the front door of the health care system. Most of that effort should really be on this maintenance of health and then you get referred back into the disease care system when, you know, hopefully early enough that really make a difference. but with some advanced warning. But the ability for us to deliver this really efficiently and low cost, I totally agree with you, is pushing this more and more to the home remotely, making it easier.

So some of the things that we've done, you know, for example, you know, we've spent the last few years developing a essentially painless, you know, at-home blood collection device used to be called the one draw, now called the nanodrop, you know, but that's like one feature of it. You're not going to go to jail like Elizabeth Holmes with this. are you not at all yes exactly that was my objection to the name change it sounds like very familiar it will be real science I have watched yeah I have got the nanotainer her little yeah I read the book I watched the documentary like

12 times I watched the the dramatization what they did of it it's a it's a fascinating story in many ways but you can move to home right microbiome testing right you can do that in your home. You can get access to this with AIs. We developed something called the microbiome wipe to make that as easy as possible for people and so forth. But the whole idea is that we should be able to deliver health information to people in ways that are much more efficient, much more user-friendly, not nearly as expensive, and that people can have a real control over their health. and be informed by really deep data. You know, I think that's really the key.

Oh, and on the, you know, coming back to, you know, some of these, you know, like small measurements, you brought up Elizabeth Holmes and so forth. One of the things that's important is that a lot of people have failed in trying to take traditional measures and miniaturizing them, you know, at least doing a lot of them at the same time. But the kind of things that we're talking about

in terms of omics, like a metabolome where you can make thousands of measures, which we're going to do on this device, a proteome that you can do, right again, thousands of measurements. Those are only ever done on small amounts of blood. So if we and I are running something on that in our lab or any of the top labs in the world, you only ever run those things on time. If you gave them a huge bat of blood, all they would do is take a tiny amount out of it and run it on the mass spec. There's no such thing as running this through it. So you're talking about technologies that are miniaturized already. That's the only way. That's

the way that they work. And so there isn't actually a technological breakthrough of any kind that's needed to use this small amount of blood to get those many measurements. The breakthrough is you have to understand how to read the information. But in the modern world, I'd much rather have an information challenge than a technology challenge because the information challenge can actually be overcome by getting access to samples, the AI is the long time. And I'll give one interesting example. So think about what happened in genomics. So in the genome, initially, one of the traits that we couldn't predict from the genome was height. Now, we all know height is heritable, right? If you have tall parents, you have tall kids, if you have short, you know,

if you're short. That depends on what you're eating. It depends on what you're, there's some other factors. But by and large, it's fairly heritable, right? So in the early days, there's no gene for height. And there's no small set of genes for height. But you fast forward. forward to now, and height is now the number one trait that we can predict with the highest accuracy. You can capture over 60% of the variance in height by a genome prediction, but that genome prediction requires over 180,000 genetic variants. So it's distributed across this long tail. So one of the things that we don't know yet is how much. You mean snips? You mean, are you talking about snips? Snips, yeah.

But you know, it's like one, it's single nucleotide polymorphism, which in English means you substitute out one nucleotide in that gene sequence that changes the function of the genes. You need 100,000 of these slight little spelling variations in order to actually predict what's going on. That's impressive. Predict high. But you could see that. There was a really interesting paper, and one of the people they included was Sean Bradley.

If you remember him, he was a, you know, a basketball player, he was 7, 6, huge outlier. And you look at this and you get a district. And he's a massive outlier. Like if you looked at his genome at birth, you could have predicted that he was going to be crazy tall. And so you can do this in the NBA. You can do it in all these different groups. And so coming back to the blood, the thing that we don't know yet is it might be possible. Once we're able to make, say, tens of thousands of measurements out of the blood instead of the handful that we do in medicine, we might find that there's a lot of information in that long tail.

It's a little harder because it's not as digital as the genome. but it might be there. And so it's an open question, but these are some of the things that are really fascinating as we go forward because there might be a ton of signal that will let us optimize health in many ways and look for early warning signs or clear them and so forth. And there is just an incredible amount of data

you can pull out of blood that we haven't harnessed yet. One of the things that is, I think, a major force right now. And we saw it with COVID in many ways is that people are taking charge of their own health care. and that they're actually very hungry to do so. And the means that they're looking for today isn't working. And this is coming at the same time where there's actually now all these tools

that do miraculous things. You see what you can do with GLP-1s. You can see what you can do with CGMs, you know, these glucose monitors. Metabolic health is such an exciting area. There's numerous areas in health that are being driven by patients and patients as consumers, not as products of the healthcare, system, but as real active drivers of it.

And that's one of the key areas that we've been interested in, and Dasey and I've been working on that space together. And, you know, we're seeing that basically, I think what's growing is a movement of like-minded companies, like-minded founders, that there's an opportunity to really transform healthcare in this way. There's many aspects to healthcare, so this is one part of it. But this part, actually, I think, is really right for disruption. And by enabling people to understand their health, whether we're talking about diet, fitness, primary care, and beyond, I think these are areas that are actually something that people are building in today.

Yeah, I couldn't agree more. I think we talk a lot about in health care, you know, problems of cost and access, but what we don't talk about is how broken the consumer experiences. And it's broken because consumers are not seen as the end customer in health care. You know, providers and hospital systems see the insurance company who pays them as their end customer and therefore don't optimize around consumer experience. And what results from that is, like, even if you are a highly motivated patient who wants to take control of your health, you, it's really hard to make appointments and get tests and understand those tests and understand what you can be doing. And then we have problems of behavior change and everyone's like, oh, that's a cultural issue. But I think what we ignore is that the best companies fundamentally change consumer behavior. And we see that all the time in other industries, you know, whether.

And so I think we're really right for consumer disruption in health care. And the function is at the forefront of that. Yeah, it's exciting. You think about what health care looks like today. And we were just talking about this earlier, but my health care records are across a bunch of different doctor's offices and different states. And it's really hard to understand what's happening in my body and how it's changing. And with function, you get, you know, your data is tracked. Every three or six months, you have all these comprehensive tests.

You can see how your biomarkers are moving. It plugs, you know, it's going to plug into EHRs and have all the data that happens in a doctor's visit, all the data from your wearable devices. And it's going to be, you know, everything that's happening in every person's body, you know, in one day in one database for them. You know, I think that's, that's an incredible vision. And one of the things that I'm curious about your perspective on is the types of innovations that are happening.

Because when I was at Cleveland Clinic, Toby Cosgrove's, one of my heroes, you know, brought the kind of discover, mentor, whatever, we call it of Watson. It was IBM's sort of supercomputer. And, you know, the big kind of tagline was Watson goes to medical school and was able to sort to ingest all of medical textbooks and knowledge and past exams and do all that great. And what really struck me was that it was sort of like rearranging the diction and the Titanic. It was using incredible technology to do the same thing better, not to do something

fundamentally different that what I would call scientific wellness or functional medicine or system medicine or whatever we want to call, it doesn't matter. It's just going to be medicine. But this paradigm shift is not, from my perspective, not really emerging from a lot of of the new startups, new businesses, new innovations that are happening. And I see just incrementalism in innovation, not a fundamental shift in how we think about health and health care and disease and diagnosis and treatment. What are you seeing come across your desk that is different?

Or are you just seeing the same kind of thing that I think I'm seeing? Am I wrong? Or this is actually how things are shaping up? I don't think you're wrong in a sense that for two factors. One is that, look, I've been changing a system as complex as health care, you know, 20% of US GDP, that's not something that's easy to do. And in fact, too, you can change, you can improve one part, but it's a complex system. That doesn't mean the whole thing improves. So the task is really hard.

And then also, there probably only going to be a few companies that really make this kind of revolutionary change. You think about the companies that, like, have revolutionized other. other industries like Spotify revolutionized music. That's something that it was basically one company that did that, or a few companies. It's not like hundreds of companies. You can go through Lyft and Uber for transportation or Airbnb for hotels. These are only a few companies. There are many that will try in a couple different ways.

But I think what will happen in this space is that a few will really stand out. And these are the ones that will be transformative. We review like thousands of companies before we invest in a year. And so there's many brilliant, hardworking entrepreneurs in this area. But making this type of change is something that only a few people can do and only a few companies will do. And those are the ones that we're looking for. And what do you think, both of you, around your vision for health care and one of the big disruptive innovations that are really game changes for us coming up? I'd love to hear your perspective because you, like I said,

have these sort of crystal ball looking to the future and seeing what's bubbling up and also understanding the complexity of health care and understanding the challenges and looking for ways to really shift. So I'd love to kind of hear your vision for the future. Maybe I'll take one area and Daisy can take another. So and we can list more. But like if I were to pick one that is the one that's been on my mind is AI. And when you think about health care, what are the big issues in healthcare right now. I think if I were to name the top three, I would call them cost, quality, and access. And AI has a hope to address each one of those. What about outcomes? That's the one I care about as a doctor. I put that in terms of quality, like the quality of outcomes. Yeah.

You know, in terms of cost, I think one thing that we're already seeing is that AI is a pilot for, co-pilot for doctors today and may take on more and more tasks. That's something that can actually, what's What's exciting about when it can be trained from the very best doctors, it can give access effectively of the very best doctors to everyone. And that's something that we just don't have today. And that democratization of medicine, I think, would be very exciting. So that would be costs and access. And in terms of quality, you know, when we saw a similar arc in other areas, like in, let's

say, on Wall Street 20 years ago, people were talking about using computers to do trading. And the reaction was like, that's ridiculous. being an expert trader takes like decades and decades, right? And there's no way a computer is going to beat a human being. You know, like, there's no way. And then 20 years later, it's like, well, that's ridiculous. There's no way a human being is going to beat a computer, you know, and we saw this in chess.

We saw this in so many different areas. And I think it's the flip that we're in the middle of now, is that it feels like hard for some to imagine that, you know, a computer or an AI couldn't do what a human being can do. but sometimes you think about what we're asking doctors to do. We're asking them to be machines to grind through all of this information, all this medical data about me and about the world, and instantaneously come up with the answer.

That's a lot to put on somebody's shoulders. But I think the hope was that AI working with doctors will be the best of both worlds. And the future in terms of cost quality and access would be dramatically improved. Yeah. I think it's a beautiful vision because I think those, those are three elements on the quality bucket I would put the paradigm shift that's happening to in medicine because you know we can do the same things better right which needs to happen and I often

when I hear about quality based care value based care it really to me is often about improving things around the margin like improving medical efficiencies reducing errors care coordination better EMRs better tracking of data you know maybe better preventive screening but it's still diagnosing the same disease as prescribing the same drugs how do how do you you think AI can play a role in really disrupting the medical paradigm itself, the scientific paradigm, not just the practice of medicine and getting people access and democratizing and decentralizing and bringing down costs and improving all of that. But how does it really change the scientific paradigm? Yeah, I think we talked about the data analysis part. I think that's part of it. But then

I think a part, and you know better than I, but I think the part of making medicine successful is giving the right care at the right time or the right place. And a, you know, helping doctors and helping medical systems make sure that happens. And this is a win for providers. You know, doctors want to make health care better, but it's also a win for payers in that if we can do that, we can keep people healthier and healthier patients are obviously less expensive, which is the win-win.

We think about what health care will look like in 20, 30, 40 years, and then we work backwards from that, and we have invested in a lot of companies who are taking on pieces of that puzzle to build us, you know, toward a better tomorrow. But I think, you know, 30 years from now, we probably 90% of healthcare is delivered via your phone. So we're going to have amazing wearable devices, both, you know, in terms of watches, rings, et cetera, but also subcutaneous that are monitoring all sorts of molecules and things happening in our bloodstream in real time. We're going to all be doing function. We're going to have at home, you know,

blood collection by then. We probably won't meet a phleotomist. We'll have a device to do it. And so we'll have a real monitoring of our health. And you were describing this earlier, but we're going to have all our health data in this one place. And you're going to be able to chat with, you know, your phone and say, I have a stomachache. What's going on? Does anything seem weird in my body right now? And then it'll ask you questions, right? Yes. And we're all going to have access to like the world's best AI and human doctors through our smartphone. And then probably 10% of health care will be, you know, going to the hospital for procedures. But more and more every year is going to be something that's, you know, you can do at home with, you know, and then we'll have, you know,

drug delivery into the home. So I think it's going to look very different, you know, 10, 20, 30 years from now. And I hope it happens faster rather than. It seems like the cost will end come way down. I mean, it seems like the cost in health care are just kind of crazy. And I wonder if you're seeing any technology companies that are creating transparency because, you know, I can send a patient. I did this not too long ago, before function, who wanted to get some lab work done. I wanted to sort of check a bunch of things. And I did kind of an abbreviated panel of what's in function.

And she, she, her insurance didn't cover it. And she sent me, he said, Mark, like, I don't know what to do. Like the bill is like $10,000. And I'm like, oh, shit, I'm sorry. Let me call the company. And so I called the lab, like, hey, you know, like, this is not our pricing. Like, you give us a different pricing. And so there's such variability in elasticity in the marketplace.

You can go to one hospital and get a scan for my knee for $400 or another scan. It's the hospital's $2,500 for the same scan and the same machine. And the consumer doesn't know any of this. And they're completely confused. I went to go get a knee exam and I need a knee brace for something. Like messed up my knee. And I get a call from the hospital today. They said, oh, just let you know your insurance didn't cover that knee brace.

And it's $1,000. I'm like, $1,000 for a knee brace. I've got a new knee. And so the elasticity in pricing is, and the lack of transparency in pricing, you know, leaves the health care so padded with costs. You know, we spend twice as much as any other development and get much worse health care outcomes. You know, we're like the bottom of the pile of development.

So how do you see kind of this evolving and us actually using technology and AI to help create transparency and kind of. more democratized health care because it's so messed up right now. Yeah, it's funny, Mark. We all work in health care and I think none of us understand how the pricing works or what we're going to get. On purpose. You know, what kind of bill we'll get in the mail?

I was actually trying to figure out if I'd hit a deductible today. And it is purposely very confusing. But I think there's a lot of promising changes on the horizon. We're getting some regulatory changes around price transparency. we're investors at a company called turquoise that's helping consumers and other entities in healthcare understand what everyone's pricing is. And so I do think we're starting to see. And you have a lot of people moving on to high deductible health plans, which is probably not a great trend in health care where you have to, you know, you have to pay out of pocket for the first

$5,000, $10,000, $20,000 before your health insurance kicks in. But the silver lining of that is I do think it enables more free market dynamics where people are going to start shopping for alert care and comparing prices. And we are, we're definitely seeing some of that in consumer behavior today. And we actually thought in relation to function, I think we thought, you know, $500 a year, is that something that, you know, most Americans are going to want to pay? And what really struck us and when you're going through all of the customer surveys is how many people were like, this is amazing value. I, something's wrong with my health. I'm bouncing around the health care system, trying to figure out what's going on. And I know these

test would cost me $10,000 elsewhere. And so you guys are obviously doing amazing things for cost and healthcare. But I think to the question about AI, we also, obviously, it's funny, BJ and I have talked about this a lot, but AI has way worse margins and is way more expensive than traditional software. But it is way cheaper than human services. And healthcare is a $4 trillion industry that's like 90% human services and a lot of expensive human services in doctors. And so I think we're going to see a lot of cost reduction from that. Yeah. I mean, it is, it is striking to me how the value we're getting is so low in terms of the diseases going up, people getting sicker and sicker, you know, rising costs, rising hospital burdens, rising disease burdens, and we're spending more and more than any other nation and getting less and less. And that can't, that can't stick.

And, you know, I, you know, I meet with senators and congressmen and I work in Washington on food. policy and health care policy. And, you know, I don't think anything even have a clear view. I said to one on the other night, I said, you know that $1.8 trillion of the entire federal budget is spent, which is about a third of the entire federal budget, is spent just on health care and not just through Medicare, but Medicaid, the Department of Defense, they need health services, VA, I mean, you name it, put it all together. It's a ton of dough and they're not even managing it. They're not even thinking about it as one problem. And so the reason I love function is that it, to me, it's, it's kind of like this little rascal on the outside of

healthcare that's trying to give people what they want and bypassing all the red tape, all the confusion, all the lack of transparency. I mean, like I said, I could, I can literally get more than two function memberships for the price of one knee brace. You know, it's like, that's nuts. The other thing that I think anyone who's gotten sick has seen or has loved ones that got sick is that you kind of have to be the one managing that process, right? you kind of like your house is a body

and you have to be the general contractor for all the people coming to help fix it and that's really hard to do but if you realize that's what's going to happen if you get sick, I think you start having this mindset shift that maybe I can do that while I'm healthy and I don't have to wait till I'm sick

to sort of be the general contractor there I should be thinking about my health I should be on top of this and we see more and more people thinking that way with, you know, for all these different reasons they come to it that healthcare is top of mind And then they start looking, and they start looking for alternatives. And I think that's the opportunity, that's the market opportunity to present those alternatives.

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