We Can Detect Cancer Years Earlier — So Why Aren’t We?

How should we really think about imaging today?

These miraculous devices we've built are important to understand because they're going to change our lives. Maybe we don't need to wait for a doctor to have already found a problem.

I kind of want to talk about this whole idea of false positives, which is something that people will push back on.

I think everybody should have a baseline.

You talked about this moment that we're in, which is comparable to the invention of the telescope, not just an incremental change, but more of a quantum change.

Wait a second, if we can see this stuff, maybe we don't need to wait for a doctor to have already found a problem. And I think that's this cusp that we're on where medical imaging is really changing.

My guest today began his career at Harvard and MIT, spent years as chief of innovation and radiology at NYU, and has developed imaging technologies used to guide the care of billions of people worldwide. He's now chief medical scientist at Function Health. This is Dr. Daniel K. Sodickson. Hey, it's Dr. Hyman. I'm so excited to share this episode with you today, but before we dive in, I wanna get your help. Please take a minute to hit that subscribe button. Whether you're watching here on YouTube or listening on your favorite podcast platform, it truly means the world to me and it helps my team and I bring you this podcast every single week. Plus, I don't want you to miss a thing. So thanks so much for being part of this community and I'm glad you're here. Dan, welcome to the podcast.

Thank you so much, Mark. It's great to be here.

I'm just in awe of you. As I was preparing for this podcast, I was like, wow, this dude's, he's got quite a pedigree. And he is rethinking how we think and apply imaging. And we're going to talk about that today, which is this sort of new craze of full-body MRIs. What's the deal with it? Should we be doing it? What are the benefits? What are the risks? What are we looking for? We're going to cover all of it, and we're going to talk about how to be proactive about your health. And we're going to talk about some of the changes in AI and medicine and some of the things that are happening on the horizon that are pretty sci-fi and wild out there, like maybe MRIs everywhere in your chair, in your bed, or whatever. I don't quite get it. But you went to Yale undergraduate and studied physics. You got your humanities bachelor's and, and phys— humanities as well from Yale. Then you got your Harvard Medical School degree, MIT degree in physics, PhD in physics. I'm like, you kind of been around. You were the head of MRI imaging at Beth Israel Deaconess Medical Center at Harvard. And now we are working together, which is so amazing. So for those of you listening, Dan and I are part of a company called Function Health. You might've heard me talk about it on the podcast. Dan is the chief science officer. I'm the chief medical officer. And together we're co-directors of what's called The Medical Intelligence Lab. And we're going to talk about what that is, what it means, and why you need to care about it and how it applies to you and your body and your health and your long-term outlook for wellbeing and how you can live 100 healthy years with proactive healthcare. And that's what we're about. It's really empowering you with the data, the information, the knowledge to actually live a long, healthy life. And you feel good now. I always say, want to feel 100%, live 100 healthy years. So that's the goal. I wanna sort of zoom out. Uh, you just wrote a book, just came out in October, um, The Future of Seeing, and it's really about the lenses we look at the world through, from the macrocosmic world of stars to the microscopic world of cells and microbes to all the imaging that we now have access to. We're just extending our capacity and our vision. Uh, and I'm sort of curious about what inspired you to write this book? What are you hoping people understand from it? And, and how, how is sort of our, our ability to see changed over human evolutionary biology?

Great question, Mark. And first of all, let me just say the awe is mutual. But no, the reason I wrote the book was that there's this sort of weird paradox in imaging now. We lead more imaged lives than we ever have, right? I mean, you can't walk down a street without being imaged by a whole series of cameras.

That's right. Facial recognition.

We lead from in utero on. And yet the mechanisms of imaging are more hidden than ever. How many people actually understand how an MRI machine works or a radio telescope works? So imaging kind of has an image problem. And what I wanted to do was give imaging back to people, to connect it to the biological vision that we evolved, to remind people that we're actually all creatures of imaging. And these miraculous devices we've built are important to understand 'cause they're gonna change our lives.

We, we really, uh, only understand the world through our senses, right? And the ability to extend our senses to look under the skin, it's pretty remarkable. I mean, vivisection, which is the human dissection of the body, was done in some, you know, ancient cultures, but often was not because the body was considered sacred. Like in Chinese medicine, they never did that. And so they, they would kind of have to intuit how things worked without actually knowing anything about what's happening on the inside. And now, you know, we had the, the sort of crude Imaging with X-rays back to the turn of the last century, and people used to go get shoes and they get X-rays to kind of look at their feet, which was a bad idea.

Bad idea.

Or people had radiation X-rays for their acne on their face. Bad idea. Caused a lot of cancer. So we've kind of had this sort of interesting history. And I'm older than you, but MRIs were kind of a new thing when I was in medical training. It was in the early '80s and it was just kind of coming on the horizon, our ability to kind of look deeper into things. We first had X-rays and then we had CT scanners. We have ultrasounds, we have MRI machines. There's other kinds of imaging out there as well. How we, how should we really think about imaging today? Because we see, you know, a lot of kind of hype out there. Kim Kardashian goes to get a scan and everybody's like, oh wow, you know, what is this about? I want a full body MRI. How should we be thinking about this?

So I think first of all, you're absolutely correct that extending our senses is a really fundamental thesis of imaging. And I would argue that every time we extend our vision, we invariably expand our minds.

Mm-hmm.

We saw that from the Copernican Revolution.

Yeah.

Basically, it was the results of imaging devices that forced us to reckon with the fact that we are living alone on this little rock. In this vast universe. X-rays completely took the world by storm, like you said, as did tomography later on. And so I—

Tomography is what?

And forgive me. Yes, tomography.

Because not all of us speak physics.

Exactly. It's CAT scans, MRI machines, PET scans.

Complicated assembly of images from different sources, right?

And really what it means, it comes from a weird Greek root. Uh, which stands for the writing of slices. And really that's what all of these modern imaging devices are doing. They're slicing through the body every which way without making a single cut.

Mm-hmm.

And I think when you, you know, you ask how to think about modern imaging, that's really what modern imaging is doing. It's capable of basically dissecting the body without ever cutting into it, and it's become this integral tool in medicine. That people use to diagnose disease, to guide surgery, all of that. But as you said, its use is starting to change and people are realizing, wait a second, if we can see this stuff, maybe we can see it early. Maybe we don't need to wait for a doctor to have already found a problem. And I think that's this cusp that we're on where medical imaging is really changing.

Yeah. Because most doctors will only diagnose you when you have a symptom. Oh, I have stomach pain. Maybe we should get an MRI of your stomach, or I've got a head pain, or I'm losing vision, or I can't walk. Maybe we should get an MRI of your brain. And what you're suggesting is that that might not be the right way to think about things. It's a proactive, preventive way to think about imaging. And we talk a lot in this space around what we call P4 medicine, which is Leroy Hood's vision, who's an assistance biologist, of how we need to think about health, which is preventive it's predictive, meaning you can kind of predict where you're going. It's personalized, so really different. And it's participatory, meaning we all have to kind of participate in our health, not just passive activity. What's happening now is that the speed of imaging, the application of AI to imaging, the innovations in imaging, the deflationary costs of imaging are all starting to kind of hit at the same time. And you talked about this sort of moment that we're in, which is comparable to the invention of the telescope in terms of our understanding of like the technological change. It's not just an incremental change, a more of a quantum change. Can you kind of unpack that? Because I think most of us just think, oh, we go to the doctor, we get imaging, and we got a symptom. But you're talking, and even me, I'm like doctor and I, I'm still curious about, 'cause I don't understand what, what you're thinking about how this change is so revolutionary.

Let me attack that basically through a bit of a personal story. 'Cause I started out in kind of a traditional way of thinking about imaging. This is the tool you use to open up the body for inspection by doctors once We want to find something wrong. And what happened is over time, as I worked more and more on optimizing, for example, these MRI machines, making them faster, making them better, I realized that a lot of the time they were being used to chase after symptoms, that we were telling people remarkable, important information, but we were telling them too late. Like, oh gee, I'm sorry, you have advanced invasive cancer. Is it any surprise that radiology departments don't get as many philanthropic donations as, say, surgery departments? Uh, we're the people who tell you you're sick, right? And then we hand over to someone else to fix it.

That's right.

And it started dawning on me that maybe there's a way we can use these tools, our kind of best tools for visualization, first rather than last. But that involves overcoming a few obstacles. First of all, they're big and expensive, right? So a lot of people say, oh, we can't do imaging you know, early because it's going to rack up medical costs. Then also, we have this weird problem that we see too much. If you put somebody in an MRI machine, you're going to find a little ditzel here or there. There's always going to be something you find which raises a question. And so this raises the whole big question of false positives.

Meaning that you see something on there that looks like something, but it's really nothing.

Exactly. Could this be something?

And then you worry, you get worried about it, and then you chase down and it creates worry and cost and interventions and—

All of those things. And those are the entirely understandable reasons people haven't used imaging proactively in the past for fear of running up those costs and creating that anxiety and giving people sort of these unnecessary tests. But as a physicist and a designer of machines, I started wondering, well, can we drive those false positive rates down? They're not God-given, right? They're not somehow attached to the devices. They have to do with the way we use the devices. And so what occurred to me after some time is, well, the problem is that we're not actually putting these images in context. We're used to getting these images and then looking at them that day, seeing what we see and saying, ah, you know, we're the, we're the wise philosophers, you know, peering at the images and saying, well, this is your future. Yeah. But if you want to predict the future, you should know the past. So what if we had previous images? What if we had a whole series of images over time? Then we could say, you know what? I see this thing here, but I know it's normal for you. And in fact, radiologists do this all the time. If they see something and it hasn't really changed from last time, they might say, you know what? I'm not too worried. Come back in 6 months. Come back in a year.

We call that an incidentaloma.

That's right. That's right. But we can actually understand incidentalomas if we've seen them before.

Mm-hmm.

And so this notion of using imaging over time and interpreting it in context became a kind of a revelation for me.

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That's right.

Right?

That's absolutely right.

And to use them in a way that measures things over time in a longitudinal way and allows you to see the change over time. And then the imaging becomes faster, smarter, better because it keeps tracking your biology over time, just like a lab test.

Yes.

We do, we do this with lab tests when we check your blood sugar and, you know, maybe it's rising, we'll then intervene early, hopefully. Or you'll see your PSA, which is a prostate cancer test, that, you know, may be slightly creeping up and we watch it and we can see over time how the change happens. We do this, but in imaging, that's not something that really is done. And you're suggesting that's something we should do.

Absolutely. And in fact, it's interesting you say that it's not done because traditionally it hasn't been, but quietly there's been this paradigm of imaging surveillance. Say, for tumors that has been building up in medical circles and hasn't necessarily been getting a lot of press. But, you know, if I go in and I have a moderate risk for prostate cancer, I may get an MRI every year and be followed. That MRI will be interpreted in context. And when there's a sudden change in the findings, then my doctor might say, ooh, you know what, we better go to biopsy. We better check it out. So people have actually realized this paradigm, but because imaging people tend to think of as a snapshot, somehow that perspective hasn't pervaded. And people still say, well, you don't want to do it in people of low risk, you know, only do it in people with well-established high risk. And my argument is, but most people out there in the world don't have a known risk. Shouldn't we be casting a protective net around them?

Yeah.

If we can figure out how to make sure we're not, you know, raising a false flag all the time.

It's interesting. I kind of want to talk about this whole idea of false positives, which is something that people will push back on, which is, and I wanted you to kind of explain this because you've written a lot about it and you've talked a lot about it. And I think we're touching on it now. And I think it's important because my personal belief is that with the radically deflationary costs, With the potentially ubiquitous nature of these imaging technologies, which we'll talk about soon, with our ability to collect large personal health data sets from your lab testing to your medical history, to gathering EMR, to wearables, to all the omics, your genome, your proteome, your microbiome, your metabolome, to gathering imaging data, people that aggregate that in a platform, a technology platform that allows you to track your biology over time and putting your biology online. Is a revolution that we've never seen in medicine before. You and I, you know, as doctors, you know, if we see patients, we get, and I had a patient like this yesterday who's got a chart from here and a chart from there and a lab from here and a lab from there and imaging test from there and imaging test from there and a scope from here and a scope from there. And I'm literally, you know, having to aggregate all this. I'm having to gather all this data. It takes, you know, hours of my time or my team's time to get it ready for me. It's not very user-friendly for the doctor or for the consumer or patient. And what we're seeing now is with Function Health, which is I think why you've kind of left your big job at NYU, you were at a big, big fancy job there and joined Function Health as a chief science officer is because you see the future. I mean, you wrote a book called The Future of Seeing and you see the future in a different way, which is where medicine is going, which is a proactive, longitudinal, large personal health dataset tracked over time that can understand that biology just doesn't change overnight. It's a continuum of dysfunction. It's slow and progressive over many, many decades sometimes that we now can see. For example, we can tell on imaging, and I mean, you can talk about this, changes that can predict Alzheimer's decades, decades before you forget your keys or you have a symptom. Should we be doing that? And people are high risk, you know, there's, there's ways of seeing changes that are really important on all these datasets, whether it's your blood sugar, your blood pressure, or your cholesterol, which we're kind of familiar with, or whether it's, you know, other things like, you know, if you have a low vitamin D, maybe you're not symptomatic. And by tracking stuff over time, we can start to really understand the human body in a way we've never done before. I'm just setting this conversation up because I want to dive into this false positive conversation. I had a conversation with a friend of mine the other night. She's like, I don't want to know. I've got Alzheimer's in my family and I don't want to know if I have the gene for Alzheimer's. And I'm like, I explained to her, look, you might have a risk gene. So APOE4, which is a risk gene for Alzheimer's, is common. And if you have this gene or two copies of this gene from both your parents, you're at a much higher risk of getting Alzheimer's. It doesn't mean you are going to get it. It means you're at higher risk. And then you go, oh, okay, I know I can be proactive about every other single thing that we know may influence the risk of getting Alzheimer's from my diet to my exercise routine, to my sleep management practices, to my stress regulation, to the right nutrient levels that I need to make sure I maintain, the right hormone levels I need to maintain if I'm a woman or a man. Like there's so much you can do, but she was like terrified to know. And I'm like, no, no, this is not a predestiny. This is a predisposition. And so in that way, I think we can kind of remove some of the fear by realizing this, with our scientific knowledge now, there's such a moment for empowerment around knowing your own data. You know, I'd love, given that sort of background, and then I want to sort of dive into the medical intelligence framework because I think the longitudinal scanning is sort of the answer to the false positives and maybe there's more, but it's also the answer to understanding your health in a better way. And it's understanding how to apply the advances in AI and medicine and science to you personally through what we call medical intelligence and our medical intelligence lab at Function Health. So to take us through, you know, a skeptic's view, I'm, I'm, I'm like Dr. Harvard here and I'm like, oh, you know, this is expensive. It's, it's too much to do. You're going to get all these red herrings. You're going to chase down all these things. You're going to cause unnecessary suffering and worry and anxiety. Why should everybody get an MRI every year? Like a full body. I do it. You do it. We do it for ourselves. You know, we want it for our families. I just ordered it on one of my staff members tonight because I think he needs it. But like, why, why is this so important? And how do we get out of this this sort of fear mode or these worry mode about, about too much information.

Even that framing is interesting, isn't it? Too much information, right? I mean, there's sort of this sense that, oh my goodness, we'll see too much. We won't know what to do with it, so let's just close our eyes. And there was, there was a time when that was appropriate, right? I mean, people often say in medicine, if a test isn't going to influence your treatment or your decision-making.

Yeah.

Your decision-making, then don't do the test. And that's actually entirely legitimate. But as you were gesturing towards, we live in a very different time than even just a few years ago.

Yeah.

Now we live in a time of big data and AI when we can collate a large collection of data and we can use AI Mark to connect it over time. To look for subtle changes, for subtle patterns at a scope that's hard for a single human mind to do.

Mm-hmm.

And so I think, you know, my recommendation isn't just go out and get a traditional MRI and have people read it in the same way they always did, looking only at today.

Yeah.

My recommendation is establish a baseline for yourself.

Mm-hmm.

And I think it's up to us in Medical Intelligence and up to the broader community to figure out how we deal with this multifaceted data. And I'll give you just a couple of examples coming from work in my NYU lab before I, I made the jump to function. So we took an AI model and trained it to predict your risk of clinically significant prostate cancer in 5 years' time based on today's images. Did an okay job, about as well as humans. Huge false positive rate, like 64% false positives. So not very good at predicting—

on the MRIs for prostate cancer.

On the, on the MRIs for prostate cancer. So not a very good, prediction 5 years out. But then we did something interesting. We took that same model and we fed it last time's images and a year before and a year before. And we also fed it some blood tests and some clinical data. And lo and behold, the more prior information and the more diverse the information we gave the model, the more the false positive rate dropped.

Yeah.

Until it was below 10%. Yeah. So an order of magnitude reduction.

That's huge.

Yeah. In false positive rate just by incorporating context. The second thing we do—

so in that sense, more information helps you make better decisions.

Exactly. So instead of, gee, we don't know what to do with it, let's close our eyes. The idea is let's incorporate everything we know. Now we need to build the models to do it, but the example I just showed you shows that it is in fact possible, even in a pretty simple prediction model, to incorporate context. And you know, I mean, as a master of functional medicine, right? Context is everything.

It's everything. Yeah.

You can't just look at one organ system in isolation. You also can't just look at one time point in isolation.

You're looking at the patterns in the data over time.

Exactly.

Patterns in the data over time illuminate the real issues and whether there's something to do or not to do.

100%. As a, you know, academic or former academic, I need to say, you know, this paradigm is still evolving. So it's not like every MRI you get is going to be put in context in this way. But in the future, that's exactly what we're aiming at. We want your MRI to be, you know, hand in hand with your blood tests and your genetics and your proteomics and all of this, because that rich context is gonna eliminate many of those false positives and give you the guide you need.

And that's what we're really building at Function was a place where you can get access to your own biology. Before you had to go through this firewall of doctors and insurance companies and maybe they would order it, maybe they wouldn't order it. You wouldn't be able to really know what's going on with your own biology. You have a dashboard for your car. Why wouldn't you have a dashboard for your body? And we're talking about establishing, you know, like those 1,000-point sensors. You know, go to your, take your fancy electronic car in and they hook it up to these machines and they just run through all these tests. And I'm like, this is amazing. We don't have that for our body. You know, and we don't have the dashboard that tells us how to navigate. What's going on in our life. And so we're often at the effect of things rather than being at the cause of our life, you know, in proactive way, empowering ourselves with the knowledge information to prevent disease and to find things early and to actually reverse things before they become problematic. One of the things that's also happened is the ability, I think, to really improve the speed and the access and the cost. So can you talk about that? Because, and I remember going to get my knee, I had a knee issue because I jumped off a golf cart and I kind of tore my meniscus and I was like, oh, my knee's sore. I'm going to go get an MRI. And so I, I went to get an MRI and it was like $2,500 for my knee. And now we're talking about $499 or $999 for a whole body MRI. So how, how is that taking us down the road to making this more accessible and affordable? And also, you know, how do we, how do we think about using that?

So here's the really interesting thing. In the future, and I think it's actually pretty, the near, pretty near future, the more we image you, the faster we can scan you next time.

Is this actually the same machine? No, it doesn't.

The faster and the cheaper we can scan you next time. And I'll, I'll give you one other example that came outta work from, from my lab. Basically what we've found is if we've only, if this is the first time we're seeing you, we need a requisite amount of data. We need the scanner to gather a certain number of views of the body to create that those slices we need. But if we've seen you before, this time we trained another neural network whose job is to take those different views and assemble them into a, a set of images.

Mm-hmm.

And we tried taking a drastically reduced set of views, 20 times less data, 30 times less data than you would need for a traditional image. In other words, 20 or 30 times faster. And we found that the neural network, if it had your prior scans, could generate a perfect high-quality image. Wow. 20 to 30 times faster with 20 to 30 times less data. Why? Because we already knew the rudiments about you and your anatomy. Yeah. All we needed to look for was change. So once we have that baseline, not only can we predict your health better, but we can also scan you faster. And it turns out Another thing we tried was, what if we use worse data? What if we use data from a low-power MRI machine, or maybe from an MRI machine we might build into a seat that would otherwise give pretty lousy-looking images? We did that simulation and we found that actually we can get away with much worse data. Hmm.

Interesting.

If we have that prior information about you. So once again, context is everything. If we have the context, maybe we don't need these big multimillion-dollar tubes.

Yeah.

Once we've seen you at least once, maybe we can put something in a chair, in a bed, in a CVS, in your home at drastically reduced cost. So more imaging paradoxically allows cheaper imaging.

But does you have to use the same machines? Like you had Siemens in one machine or GE another machine. Can it kind of, how do you, how does it gather that data from the past?

So there's a logistical challenge of how do you bring your past images from another machine into today's machine so that it can do this. But that's just logistics. I mean, nowadays we have, you know, digital image transport systems and, and so on. But what we found is it doesn't need to be, the image doesn't need to be exactly the same last time as this time. In fact, we used different contrasts last time, and it still informs, you know, your imaging this time. So this is actually part of something I think you may have referred to it before that I call the everywhere scanner vision.

Yeah.

If we have enough information about you, if we've done the advanced imaging, the advanced blood testing upfront, then for the interval scanning, maybe we can use cheap scanners. Maybe we can even use constellations of wearable devices on your clothes. Because all they need to do is measure change.

That's amazing.

Which means we can move healthcare not only more proactive, but also make it more continuous.

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Yes. Yes.

The, the, the, the tricorder, the tricorder, the scanner for the—

I, I would just tell you the tricorder has been like, um, a holy grail for imagers forever, right? 'Cause it's this tiny little handheld device. You wave it in front of something. Somebody, and you get everything you need. I actually think, and I talk about this in the book, I think the tricorder is a bit of a trick. I think it's actually not its own device doing imaging. I think it has access to all of the records that Starfleet Academy had on you.

Yeah.

And all it's doing is looking for change.

Yeah, that's amazing. That's amazing. So, um, people understand that they can get blood work and, and know a lot about their bodies and A lot of people have joined Function as members and are learning so much, and we're seeing so much in the population that people are discovering that saves their lives from cancer or figures out they have autoimmune disease or figures out they have other problems that are really fixable. How does imaging differ from blood work and what are we looking for? Because people are like, "So I'm looking for my cholesterol, my blood sugar, my hormones, or my vitamin D level." You know, whatever my blood count and my immune system, but what, what are we actually looking for and how is it different from blood work? And then last question, intro, how do you, how do you think the two together are better than either alone?

So I think blood tests give you biological and chemical context, right? It's the various biomarkers that your body is producing that tell us about the biological functioning and systems. Imaging is spatial context, right? I mean, if we were just undifferentiated bags of chemistry, then blood tests would be enough. We wouldn't need to know anything more. But we all know that bodies are sort of these complex bio-weavings, and it matters what's where, when. And so imaging, as I see it, is what puts all of this chemistry in context, in spatial context, which leads naturally to the question of synergy. Like you want both, right? You want to know what's where, and you also want to know what's the biological functioning in each position. And when you've got both, you sort of have this magic mixture. So what do we do?

Structure function.

Structure function. Exactly. Um, and so what do you look for in imaging? Well, you look for tissue that's out of place, right? A tumor that might be growing where it shouldn't. You look for derangements of the brain that tell you hints of, you know, Alzheimer's disease, things like that. Things that there may not be a circulating counterpart. There may not be something that was spit off and sent into the bloodstream. And so you can measure it in, in a blood test, but you can see it in situ. You can see it where it is.

So if you have an aneurysm, there's no blood test for that.

Exactly right. I, I think this combination of biological, biochemical, and spatial without context is really, you know, cooking with gas.

We are both part of Function Health and we do imaging as part of the offerings we have. I think it should be part of the, you know, ultimately just part of the thing that everybody does, which is not just the blood work, but also the imaging. What are we finding? Tell us some stories about what we're finding. Cause you know, you've been working with Ezra, which was a company that became part of Function for a long time. And you've seen a lot of stories and we're seeing crazy stories of people. What are people discovering and what are they finding?

Absolutely. And I'll preface it by saying, you know, I know there are going to be some physicians out there who say, you know, any story I come out with, it's just an anecdote. It's not, it's not, you know, randomized controlled trials and so on. I'll get back to that later because I think there's an answer for that.

Anecdota.

Anecdota. But no, I mean, the obvious things, clearly we have found tumors that people didn't know they had.

And early before they— it kills them.

Exactly. And that's the key. I mean, you know, our, our friend and colleague Emmy Gall likes to say we already have a cure for cancer. It's early detection.

Yeah.

Because most cancers, if you catch them early enough before they've become invasive, they're that much easier to get rid of with radiation, with, with chemotherapy, with surgery. So we have found certainly prostate cancers, brain cancers, kidney cancers at such an early stage that they weren't giving anybody symptoms. That was the whole point.

Mm-hmm.

But what that meant is these people could then go in for therapy right away. Long before these things would've been discovered.

Yeah.

And it is saving their lives. There are any number of other kind of body areas where you can pick these things up. Ezra had a particular focus on cancer, which is sort of obvious because early means life.

But we can see the change in the brain function structure. Absolutely. We can look at brain size changes. We can look at the structural pieces of the brain that change over time that could be linked to different diseases like dementia. We can also see, you know, interesting things in terms of body composition, fatty liver, cardio—

cardiovascular health, right? Cardiovascular health, right? Coronary artery calcium scans have been shown actually with very good data to be predictive of cardiac cardiovascular risk.

Right.

And that we can see in a very straightforward way. Combine that with some of the cardiovascular biomarkers. Right. And again, you're cooking with gas.

Yeah, I think that's it. I think, you know, the combination's important. And I remember, I don't know if you remember that textbook we had in second year medical school called Robbins and Cochrane.

Oh yes.

The patho is called The Pathophysiologic Basis of Disease. And I went back, I still have the cop— my copy from like 1984.

Me too.

Me too. We have a colonoscopy, we have a mammogram, we have, you know, things, PSA we screen for, but like most cancers we don't even screen for. And when you combine that and those two, and I think emerging proteomic data, which is coming when proteo— proteomics are basically proteins that the body makes and these cancers spit off these proteins that we use sometimes already. To detect cancer or follow progression like alpha-fetoprotein or CEA, CA125 for ovarian cancer. These are things that we've been using in medicine a long time, but they're used kind of late. They're used to manage the disease, they're used to track progress. But if you combine these and using AI, and this is the amazing thing about this data, this is coming soon. These large databases of cancer survivors, and they have biobanks where they collected their blood. They've been able to go back and say, okay, well let's look at all the patients with lung cancer, all the patients with pancreatic cancer, all the patients with colon cancer, all the patients with prostate cancer, all the patients with breast cancer. What do they have in common? I mean, within each cancer. And then they can go back and check this blood and screen 5 years ago and see these proteins that get expressed. And they're able to, through AI Mark, make sense of all that. Because it's like if you've got millions of data points in, the average doctor can't, well, no doctor, even a brilliant doctor can't, like you can't sort through all that. And so using, like you said, big data and AI with our understanding of biology, we're entering a new era of medicine. This is the era of medical intelligence. That's what I'm talking about when I say that.

I agree entirely. And I've obviously voted with my feet to that effect.

You have.

Can I get back for just a second to the kind of clinical trials question?

Yes.

Because that is one of the things that gets thrown out a lot as a concern. Like, okay, all of this is wonderful in principle. It makes sense, but where's the data? And should we be proceeding until we have the data? And I actually want to go back to another time in history, the 1970s, when all of these tomographic imaging techniques, MRI, CT, PET, ultrasound were being developed. Back then, the value of knowing what was where in the body was obvious. So there were 1,000 CT scans in hospitals between 1971, when CT developed, and 1979, when the inventors got the Nobel Prize for it. There were no large-scale clinical trials showing the efficacy of seeing versus not seeing. Now, I'm not suggesting we should throw caution to the winds. We should absolutely be gathering data as we go. And in fact, Big data allows us in some ways to do almost real-time trials as we go. But to say, listen, I'm not going to do anything until the data is there. I think that's one extreme of a kind of spectrum that we should be thinking about. I think there's this kind of protective instinct, which I, as, as somebody in medicine, I believe in, but I don't want to be protecting patients, protecting people from this new era that's coming.

Yeah.

I want to figure out how we, how we make it happen as quickly as possible and measure as we go.

Yeah. I mean, how do we not get ahead of ourselves? But you know, in a perfect world, we would, we would bring the cost way down. We'd allow people to access large data sets to themselves. We'd be able to track that over time. We'd be able to see where they're headed and what to do about it. And that's really what Function Health was designed to do. That's why we created the company, was to empower people to be the CEO of their own health, to be empowered to own their own data, to be able to have a data-driven healthcare and medical system, and to use big data and AI analytics to understand all this massive amounts of information. How many gigabytes or terabytes is like a full dense MRI bot? It's like a lot, right? You can't even put it on your computer.

That's right. It's a bunch of gigabytes per person per session.

You know, what I want to sort of have people understand is like, Who should and when should somebody think about starting to get their first baseline MRI? Is it when you're 20 or 50 or 100?

Right. Well, again, it's hard to point to a, a data-driven age because it varies for the particular thing you're looking for and all of that. I guess I would reframe it and say, I think everybody should have a baseline. A baseline scan because, you know, and okay, maybe not. Well, the body is still developing when you're, you know, 5 or 12 or something, although, you know, there's some argument there too. But the whole point is we want to be able to measure change in your body. We want to be able to know what's normal. And so I think at the very least, that reference scan there's no reason for that not to be done early.

Like in your 20s.

In your 20s, as long as the people who are interpreting it aren't jumping the gun and freaking out at everything they see. So the problem with that first scan is we don't yet have the context. And so there's a tendency then to follow every lead. If— and this is another sort of paradoxical thing. If we know that imaging is going to be regular, then we don't have to freak out at every finding. So in other words, we get a baseline and we say, okay, we're going to see you again in a year or 2 years to make sure we've established not just one point, but a trajectory. Even just that second scan is already going to rule out most problems. So I think that, you know, We're heading to an era when people should have a baseline and a sense of trajectory relatively early so that we can establish this basis for change.

So then how often should someone do a scan? Yearly? Or I mean, for example, I'm 66. I kind of want to do one every year, right? Does that make sense? But what if I'm 35? Do I want to do one every year?

And again, the scientist in me is, is pausing because, you know, I'm not— I don't have studies to point to. But from sort of basic logic, my feeling is yes, more frequently as you get older and changes are more likely, a little less frequently when you're very young and, and changes aren't that likely. If we can, let's put it this way, if we can get the cost of something like an MRI scan down enough, and if we can make sure that we're not overcalling things, then there's no reason not to have an absolutely regular scan. Let's say 2 years, every 2 years when you're younger, every 1 year when when you're a little older. I want, and you know, you know, with the Everywhere Scanner vision, I want imaging to be kind of an ongoing intimate part of our lives. Not this thing that we do just when we are worried, are we sick? I want it to be the thing that tells you you are, you are still okay. Not the thing that only tells you that something's wrong. Yeah. I want it to be a safety net. It's not, you know, an end-stage tool.

There'll be like tools and devices and things that we can have to put our biology online in real time and, and see what's going on. And, and it's kind of combined with this idea of an everywhere scanner is, is really very futuristic. Mm-hmm. So talk about this idea of the everywhere scanner and how it, how it's gonna change how we think about our health and medicine.

I really think of it almost like building ourselves a new augmented artificial sensory system, right? We have multiple senses. In fact, you know what? It's not sci-fi at all. We already have continuous sensing.

Yeah.

We've got our entire nervous system.

Yeah.

We can sense temperature and pressure and pain and all of these things. And these sensors are woven throughout our body. Yeah. The only problem is they're really not great at giving us early warning of internal things that are going wrong. They're really good at telling us, don't touch that hot stove now.

Yeah.

But they're not giving us advance warning of cancer. That's not what they evolved to do. I think what we're talking about with Everywhere Scanner and with abundant sensors is basically building that artificial nervous system that's giving us early warning of all kinds of other biological things that we just didn't happen to develop nerves for. Yeah. And I think the body, quite frankly, it's a remarkable piece of engineering. I think we should pay attention to what it's built. Certainly in imaging, almost every innovation in vision that has evolved has been copied and improved upon with an artificial imaging device somewhere. Every single thing that the eye does, we can learn from. And that the brain does in processing vision. Likewise, I think when we think about this network of continuous sensing, we should look at what the body's built and build on that.

It's kind of cool. I mean, I rented a car recently and the thing just senses everything. It's like I drive under a bridge and the Google Maps turns a different shade, or I'm driving down the road and there's no car in front of me, it turns the brights on. And when a car is coming, it turns the brights off. Or when, like, you know, every little thing. I literally took my hands off the steering wheel and said, hey, put your hand on the steering wheel. It looked in my eyes when I looked away for something. I was like, oh, make sure your eyes are on the road. I'm like, wow, this car is like spying on me, but it's sensing everything all the time all around it. And, you know, kind of like a Waymo or a Tesla, which, you know, does self-driving, it's the same thing. And so what we're talking about is augmenting the sensing of our own biology through Various kinds of tools, whether they're intermittent or continuous tools, allow us to put our biology in a different context and to understand it over time and to not have this episodic, often too late to the game diagnostics, which unfortunately, you know, with medicine, when you find things too late, it's often hard to fix, right? And so I think particularly around cancer, you know, my father died of cancer, my sister died of cancer, she had cancer twice. You know, I don't wanna die of cancer. I don't wanna, I wanna live a long, healthy life. And I feel like it's one of those things that we now actually potentially with the Galleri test and liquid biopsies, the regular imaging and even the proteomics that are coming, we literally could make cancer and dying of cancer a historical foot.

I believe so. And I hope so.

That's really the part of the mission of Functional Health is to do that and to relieve so much suffering. 'Cause there's so much suffering with cancer and I, you know, you, I just, you see it all the time and it, I know it in my family and I've seen my own relatives just wither away and die. And it just, it's such a heartbreak and it's, it's, um, in some ways, you know, if we had this proactive preventive approach to medicine, we wouldn't be in this situation.

Absolutely. And, and my family has had that type of cancer history as well. And I, I just wish that, uh, we had had these tools earlier.

It, it's giving us insight into human biology in a way that we've never had before. And we're able to then on top of that, apply the 39 million scientific papers that have been published on PubMed to filter and understand all that information. They're taking the, you know, all the sort of case studies we can apply, all the training that we've done based on root cause medicine into the system. And so when you put your data into Function, you're actually putting your biology online. And combined with these large language models and the advances in those we're seeing every day, we're entering an era where we're really truly being able to understand the body in a way we never have before and look at the patterns in the data and create an early, early assessment and continuous monitoring over time rather than this episodic kind of random checking to really know what's going on in your body and then to be AI Mark, able to sort of understand the subtle changes, the differences, to look at the patterns and data, to learn and to advance science, to help individuals with their own issues. It's really quite amazing. So I would love sort of you, uh, for you to unpack your vision of the, what, what we're doing with the Medical Intelligence Lab, where, where we're headed and what we want to build in the world. Because I think this is really foundational, revolutionary to, to medicine and science itself. And I think it's going to change everything we know about human health and biology.

I think of it a little bit like a GPS for health. And if you unpack what a GPS does, it actually has a lot of the features that you talked about. First of all, you need a map, right? That's all of the accumulated medical knowledge.

Yeah.

That you're talking about. You need to know what the landscape is like that you're navigating through. Otherwise you're going blind. But more than that, you also need to know your personal history. That's your biology that you've put online, right? Because if you don't know where you've come from, you know, um, you, you don't know what road you're on. You, I mean, you kind of need to know that individualized information, not just the collective information. And then the key thing, which I think we're really working hard on in, in the Medical Intelligence Lab, is how do we create that guideline that gets you where you want to go, that travels with you? And make sure you get to 100 healthy years. And that involves then taking all of these patterns that we've learned, the population-wide patterns and the individual patterns based on your data and projecting them forward and making predictions. Hey, listen, if you just keep on steering this way, you're headed for trouble. No, maybe you need to do a little course correction, change your diet, change your exercise, you know, go in for another test at this interval, that type of thing. And I think it is definitely a remarkable time when we can think about creating that sort of comprehensive GPS. You mentioned that big tech is already, you know, gunning for this space. It's happening regardless of whether, you know, we in medicine are comfortable with it or not. I sort of see it as both our responsibility and our privilege to try to bring the science of medicine to that endeavor rather than just feeding lots and lots of data to, to chatbots, really trying to bring the collection of medical knowledge and the knowledge about integration of body systems and the context as you're talking and the context and your individual context.

Yeah, that's what I mean.

Yeah, exactly. This problem so that we're not just generating a nice-sounding set of answers to questions. We're actually providing you with a guide. We're giving you that map to your health.

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And in fact, going back to this model of biological senses, you had talked about the concern that some people legitimately have. Well, I don't wanna be anxious all the time. I don't wanna be thinking about the diseases I might develop. If you think about our senses, they evolved to protect us from harm in a similar way. We don't think about them all the time. We just get this burst of alarm if we step into a street and we see there's a car coming.

Yeah.

But most of the time the senses are just operating in the background, keeping us alive.

Right.

That's how I see this online biology and this network of sensors in the future. It's not constant alarm. It's just waking up and giving you a ping if you're about to step into the street with a car coming.

Yeah.

Medically.

Yeah.

That type of safety net is something that even the most kind of squeamish people might be comfortable with. It's just, you know, for the moment, don't do this because it's gonna harm you, but otherwise live your life and live your life well.

That's right. And I think, I think we know so much now about how to prevent disease, and I think people are worried about finding out something that they can't do anything about. And I understand that, but most of the time, you know, your biology is changeable and there are early detection signs that are, as I mentioned, these biochemical changes and these then turn into pathological early changes we can see on scanning that, that, that really give us a roadmap to what's happening with our health. And it, it, it putting our head in the sand and not paying attention and not looking at our own personal data. It doesn't make any sense. Now you go, well, I'm not a doctor. How do I make sense of it all? And you know, yeah, you're right. It's, if you don't know how to sort of make sense of it all, then it's a lot. But if you have the facilitation of a company like Function Health that provides you with the guidance, provides you with the intelligence behind it to make sense of it, to create a ranked order priority list of what you have to address, to help you understand what the steps are you can take yourself when you need, you know, to do self-care and when you need to see, seek medical care and provide that whole continuum of care for you rather than just sort of waiting around until something's happening. That's the thing most people don't realize is that disease doesn't just happen. It's occurring because of low-grade changes over many decades. And the thing I want to sort of, sort of end with here is our bodies are this highly intelligent system that want to be healthy. Your body is not designed to be sick. It's not a design flaw. We are, are providing the conditions in our current modern society for the body to be sick with the crap food that we're having available. 73% of the food on grocery store shelves is not even technically food. It's ultra-processed Frankenfoods. And we have enormous amount of environmental exposures and toxins that sometimes we can do things about and actually help our bodies detoxify. We have, you know, dysregulation related circadian rhythms and sleep. We have excess chronic stress. We have all these things, sedentary lifestyles. These are things that we are empowered to do something about. We have nutrient deficiencies, which you can do something about. And when you, when you actually can know what's happening early, then you can, you can make changes that really change that course and allow your body to provide the conditions that are gonna create health rather than simply waiting till you have to really treat some serious disease. And this is, this is a fundamental paradigm shift is, is the idea that disease isn't just some random phenomena. It's something you can predict from early indicators and then do something about. And I just saw a patient yesterday with Parkinson's disease. He'd been, you know, had warning signs way early. He had tremendous amounts of environmental exposures from hobbies and being in the Navy as a chemical engineer and in childhood. And I'm like, this guy's in would be a sitting duck for some type of toxin-related illness. And Parkinson's is a well-known toxin-related condition. And yet he had to wait until he got Parkinson's for someone like me to look at his history and go, well, gee, you know, we gotta, we gotta get all this crap outta your system and we've gotta detoxify you. And, and, and that was something that he didn't have to necessarily do if he'd been proactive and actually was able to measure the toxic load of his body early on. And first, same thing happened to me. I had heavy metal poisoning from I'm in China, but I wasn't sick right away. It was like this kind of slowly building up burden of toxins that then knocked me off my feet. But if I had known early, I could have done something about it and not ended up in this catastrophic illness. So I think we can actually see these changes over time. We can do something about them if we have the right information, and he just didn't have the right information. So that's really why I think medical intelligence is such an important concept. And our medical intelligence lab at Function Health and the science we're putting behind it and the effort we're putting behind really providing the best quality understanding information of your biology is, is gonna change medicine and healthcare.

Here, here. No. And, and listen, Mark, I mean, we started the conversation with the future of seeing, right? In some ways, I think in a nutshell, the future of seeing involves actually looking. Yeah. Now that we have the capability, now that we have the capability to see lots of your biology, now that we have the capability to use AI and other similar tools to integrate that, to connect it to knowledge that has been accumulated over all of these centuries. Now is the time when we, we need to start living with our eyes open and living with that kind of guidance.

So The Future of Seeing, your book, which everybody should get a copy, where can they find it?

Uh, they can find it, uh, at Columbia University Press or on Amazon, of course. I love it.

Columbia University Press. That's beautiful.

I love it.

Of course. It's a great title, The Future of Seeing, because it's not literally just about imaging. Your book is about imaging, but it's also, you know, implies that the future of seeing is about the future of seeing deep into human biology in a way we've never been able to do historically. And it will transform medicine healthcare from the outside in because traditional healthcare is not changing anytime fast. The edifice is too solid and the resistance is too much and the old ideas die very hard. I mean, I think, you know, there's a book I read in college called The Structure of Scientific Revolutions. By Thomas Kuhn.

Yes.

And in this book, he talked about this idea of a paradigm shift. And he, he, that's where the word paradigm shift came from. And in the book, he talks about this idea of normal science, that what we believe is just so embedded that we can't unsee it. In other words, if you were living in, you know, 1400, the earth was flat. If you were living in the pre-Galilean era, the earth was the center of the universe, right? This is something now that, that we, we, we have to understand because we are, we are living in a totally different era where we can actually see things that we never could see before. We can look where we never looked before. I mean, look, I remember, I mean, let's see, it was, it was 13 years after I graduated from medical school that we decoded the human genome. So, I mean, this is in a very short time. And that was a billion dollars. Now it's $200 to decode your own personal genome. That's where we're going. We're going to this massive personal data-driven healthcare system. And I think in a way we're disrupting healthcare because we're going to empower people to be in a way their own healthcare agent. And then yes, use medicine and use hospitals and use surgery and use doctors when you need them. But most of the things that we, we pick 'em up early, they're fundamentally things that are under our control. It's what we eat, it's how we move, it's how we sleep, it's how we manage stress, it's our relationships, it's our toxin exposures, which you can mitigate to some degree. Those are all the things that are driving disturbances in our health. And those are things that we can pick up in these early warning signs, like your car. Okay, your tire pressure's a little low or your engine light's a little thing or whatever. Like, I don't know, these sensors are amazing on these cars. And I, wouldn't it be great to have that dashboard for your body? And that's really what we're doing with Function Health and it's just gonna get better is smarter. So I encourage everybody to, you know, not just because I co-founded the company, but I encourage everybody to think about how do you put your biology online so you can be proactive about your health and not get that horrible sinking feeling in your stomach when you're in the doctor's office and they say you've got metastatic cancer. You know, Chris Van Der Beek, I think, I think it was his name, was this actor who recently died of cancer. And, you know, he didn't need to. He really didn't need to, you know, my sister didn't need to, my father didn't need to. And we, I wish this technology was around then. And I think this really what we're talking about here, Dan. So any final thoughts or words for people listening?

I think you said it beautifully, Mark. I think really my final words are in this remarkable era, keep your eyes open, get that biology online, figure out how you can Essentially have this new safety net that, that nobody in the history of humanity has had before.

Yeah. Amazing. Well, thank you, Dan. Thank you for your work. I'm excited to work with you building the Medical Intelligence Lab and keep Function evolving and helping it to actually help billions and billions of people. I think we're just getting started. So people should stay tuned. You can learn more about Dan's work through his book, The Future of Seeing. Go to functionhealth.com to learn more. It's only a dollar a day to join as a member. And that will give you a deep dive on your biology and you can get a full body MRI scan as a baseline through that website and even more deeper scans if you want for other things. So I'm really excited about what we're doing together. I think combining the ability to gather your history data, your EMR, your wearables, imaging, lab data, all putting it together and helping people understand their biology is really revolutionary. I'm so super excited about it.

Thank you so much, Mark. It's a, it's a pleasure and a privilege to talk with you and to work with you.

Amazing. Well, thanks, Dan. If you love that last video, you're gonna love the next one. Check it out here.