D (10:45)

Thank you, Derek. It's great to be here.

B (10:47)

I'm really grateful you're here. There are so many wild claims flying.

C (10:52)

Around about what artificial intelligence will be able to do for us in medicine.

B (10:56)

It will diagnose all our diseases.

C (10:58)

It will help us design new life saving drugs.

B (11:01)

It'll accelerate clinical trials so we can test every drug that we invent faster and cheaper. And it will join with wearables to.

C (11:07)

Fight chronic illness and extend our health spans.

B (11:09)

These are incredibly dramatic claims for technology.

C (11:13)

That is still in its infancy.

B (11:14)

And what I want to do with you in our brief time together is interrogate each of these claims very directly and narrowly to understand what can this technology actually do for us right now. Right, like the future's a foreign country. Like if AI cures every cancer in 2031, I will report in 2031 that AI has cured every cancer, but it hasn't done that. So I don't want to get ahead of our skis. Does that sound okay? As a general landscape of the show for you.

D (11:39)

It sounds great, Derek.

B (11:41)

All right, so four claims that I.

C (11:43)

Just outlined and that I want to scrutinize in greater detail.

B (11:46)

Claim number one, AI will help us diagnose disease more efficiently in a way that will save lives and money. And to give a little bit of context for my understanding of this space, in the last few years we've gotten studies from mit, Harvard, Stanford and other schools that have repeatedly found that ChatGPT.

C (12:05)

Doesn'T just equal doctors at diagnosing several illnesses.

B (12:08)

It beats doctors, including sometimes doctors who.

C (12:12)

Work with AI to diagnose diseases. Just this year Microsoft released a paper that found that its technology, quote, solved.

B (12:20)

More than 80% of case studies, which was about four times more than than.

C (12:24)

The human doctors that it surveyed. Now that's a Microsoft study. They're of course going to say that their technology is four times better than humans. But that's the context.

B (12:31)

Is this effect real? Do you think AI is already a better diagnostician than most human doctors today? And what would be the significance of that?

D (12:42)

Well, Derek, I think first of all at the broad level, and you outlined four very exciting topics that we're going to cover in our conversation today. I'm reminded at the beginning of our conversation, I'm reminded of the statement attributed to many that we tend to overestimate what we can accomplish in the short term and underestimate what we can accomplish in the long term. And I think perhaps we're seeing some of that play out today as we deploy AI in all aspects of life. But in particular what we're talking about today, AI in the application to health, healthcare and discovery science, life sciences. Now on the first topic of how is AI impacting today? AI impacting the diagnosis of diseases and the selection of the correct treatment for diseases. And in general, how do we get identify the right patient for the right therapy at the right time? I'm reminded of, you know, I'm probably best known in my field, which is the way the inner ear balance system works and, and in the field of otology and neurotology because I described an inner ear disorder in 1998, developed a surgical procedure to fix it. For many, many years after those first papers were published in which I, my colleagues described the syndrome and then had a bunch of follow up studies. Still most of the patients coming to see me diagnosed themselves from doing a Google search at the time. And you know, there was a lot of concern when the Internet first came widely available and people were talking about Dr. Google and how Dr. Google is going to get us in trouble. And for sure, there are examples of that. But the fact is, I think if you look on balance, and we're now, before we talk about the AI era, just the value of search. Search has really democratized access to medical information and I think has helped many people and many physicians. But fast forward now to AI and everything that I think search has enabled us to do and improving access to information and then applying that information to diagnostic situations, I think we're seeing that the effects of AI and taking that to a whole new level. Is it going to eliminate the role of a physician diagnostician in the near future? No, I don't think so, but just in the short years. What ChatGPT was introduced in November 2022, just in the relatively short period of time, less than three years since its introduction, we've already seen evolution of the models. We've seen fewer hallucinations, and we've seen more accurate, focused information coming back for them. Today, I think many physicians are using large language models to assist in diagnoses. What we've done here at Stanford and many of our colleagues at other institutions have done the same, is that we've in licensed models so that we can actually put elements of the medical record into a large language model, but in a secure, closed environment, so that there's no breach of patient privacy. But in order for physicians to get information back on a series of rare findings in a patient. And how do you fit all those together? And in particular, we. When you're selecting a therapy and you're selecting a medication, and maybe you don't know what the probability of an adverse reaction to that medication is in that patient, because that individual patient has a constellation of medical findings that isn't necessarily readily apparent in the knowledge base of the physician or in the medical record.

C (16:29)

I think it's thrilling to think that artificial intelligence has access to this digital cosmos of symptoms and disease and etiology, and can be a genius at answering questions that would require a typical doctor to find a needle in a haystack. I mean, at a very personal level, my dad died of a vascular cancer.

B (16:47)

For which the entire medical literature had.

C (16:50)

Something like 12 cases.

B (16:52)

And so if there's an artificial intelligence that can see those 12 cases with a clarity that a human might not.

C (16:59)

Be able to, my dad might be alive.

B (17:01)

So I come at this from a place of extreme hope. That said, I have two worries, and I want to make sure I articulate these worries clearly. One worry is over diagnosis. So I think about artificial intelligence interacting.

C (17:15)

With the existing financial incentives in healthcare.

B (17:18)

And if AI is incorporated into a system that rewards treatment, then its ability to find the tiniest little blips in radiology or pathology reports seems to me to be very likely to lead to too much treatment.

C (17:33)

Some of it necessary, some of it.

B (17:34)

May be unnecessary, some of it may be harmful. And one could imagine how that would lead to, say, healthcare costs going up.

C (17:40)

In ways that don't necessarily extend people's lives.

B (17:43)

Are you worried at all about doctors who don't yet entirely understand how to use this technology, Using it to find the tiniest little cysts that mean absolutely nothing to people in a way that raises healthcare costs and adds over treatment?

D (18:01)

Yes, I think it's a valid concern, and I believe we've seen that risk and the manifestations of that risk with the introduction of every new medical technology. For example, now, a standard way to evaluate the function of the heart is through an echocardiogram. It's a non invasive study, involves no exposure to ionizing radiation. The equipment and the technology has become very sophisticated. You can get accurate measurements of the size of each of the chambers of the heart, the pumping function of the heart, the thickness of the wall of the muscle, the diameter of the valves in the heart. Huge amount of information. And guess what, there are findings, probably more commonly than not, the valve size is a little bit different, or the aortic root is maybe a little bit larger than we typically see in a person of that age. And then what do you do with that? So that's. And we go through waves where perhaps until we get enough medical knowledge to know that that slight enlargement of the aortic root or whatever is not that significant. And maybe the echocardiogram should be repeated in a couple years or five years, but nothing should be done at the time. But until we got that knowledge and understanding, there were probably people that did undergo further workup and maybe even some that underwent procedures. So I think any new technology has that concern, that risk, and it ultimately is dependent upon its responsible use. However, echocardiography has vastly improved our treatment of common cardiac diseases, Enabled us to know when we need to intervene, enable us to know how to adjust medications, how to judge a patient's risk factor for a variety of different conditions later on. So I don't think we would do away with it. But there's been a curve, an evolutionary curve of understanding how it can be appropriately used and how findings that may deviate from the quote, normal need to be interpreted in the broader context of the patient's health and of the status of the heart.

C (20:12)

I really like that answer. And one way that I'm synthesizing it for myself is that I think some of the most booster ish predictions about AI say oh, not only is it going to find all the diseases, it's going to save us so much money. And there's a tension in there.

B (20:25)

If AI helps us find more illness, it will probably lead to more treatment. And in the capitalist healthcare system that we have, more treatment is going to cost more money rather than less. So it's not as if we're going to enter this, I think, Valhalla, where healthcare costs plummet, we find all the diseases and everything is hunky dory forever. It's possible that the actual real success of AI at detection of will raise.

C (20:49)

Overall healthcare costs in some ways rather than reduce them.

B (20:53)

The second thing I wanted to push on is I'm interested in the idea that some doctors probably won't use large language models for diagnosing enough, but some doctors might use it too much. So, for example, there was a paper.

C (21:08)

That was published in, I believe it was Lancet, Gastroenterology and Hepatology which found.

B (21:13)

That after three months of using AI to detect cancers in colonoscopies, doctors reportedly got worse at finding growths on their own. And I think that's so interesting because so much the literature on AI and.

C (21:25)

Medicine is about right, these Luddite doctors rejecting the technology when it could make them better.

B (21:29)

But here we have evidence of some doctors accepting AI, but it makes them it de skills their human intelligence. Is this a valid fear? Or maybe another way to put the question is, are you more afraid that doctors won't use this technology or that they'll use it so much in a way that they're actually deskilling themselves?

D (21:52)

Yes. Let me return to your first point just to make a concluding remark. I do think that's a risk in terms of AI actually driving healthcare costs up. And we've seen that in a lot of cases with the initial introduction of new technology. However, the real hope and opportunity is that AI will ultimately lower health care costs because we will diagnose diseases earlier and therefore treat them more effectively, effectively. So, you know, in the United States, we spend, we have a great sick care system, we spend a huge proportion of our medical expenditure is on care of complex diseases. If we could reduce some of the complexity because we've diagnosed and intervened earlier, or maybe in some cases figured out specific preventative strategies based on our risk factors that AI enables us to identify, then ultimately we can lower healthcare costs. And so that would be my concluding thought on our first topic. Now, the second topic. Absolutely. I worry a lot about that as a medical educator. How should we be educating the physicians of the future? How should we, those of us who are practicing physicians, be thinking about the responsible use of AI as we have more and more knowledge, not only information, but knowledge, at our fingertips? You know, medical education, the best analogy I know of has been it's somewhat akin to learning foreign language. What do you have to do to learn and communicate in a foreign language? You have to know the vocabulary, you have to know the grammar. Then you've got to be able to synthesize and put together the vocabulary and the grammar in order to communicate with someone else and interpret what you're hearing back from them or reading, in the case of reading the text in a foreign language. In medical education, we have to know the vocabulary of medicine. We have to know the grammar, which is how the body works, the biochemistry, the way the organ systems interact with each other. And then we have to put that together when we're seeing a patient to synthesize a diagnosis, come up with a treatment plan, and then, you know, carry out that treatment plan, obviously, in combination with the patient and their family. Now, where along that spectrum will AI be a disruptor? Well, in terms of the vocabulary, knowing sort of the medical information, as you pointed out, AI is really good at that. It's only going to get better. There's no turning back. So. And we'd already started to move away from, thank goodness, some medical memorization. I mean, when I was in medical school, not only did we memorize, you know, the names of drugs, their mechanisms of actions, we had to memorize their dosages. And that was terrible because human brain is not, you know, isn't designed to keep arcane numbers accurately. And the, the consequence of making an error is significant. Well, now, of course, we don't do that because dosages are generally calculated based upon the patient's height, weight, and other characteristics. And we maybe to have some general knowledge, but that's taken care of with a lot of checks and balances in the background. One can envision AI, you know, taking that a few steps further and actually identifying what the correct medication is. And then, and then the, the, the real issue from the standpoint of the role of the physician is how do you evaluate that information coming back from AI to know, yeah, this, this seems right, or no, I don't think so. And I'm going to push it a little further. I'm going to question it. And, and the risk is, as you said, that we become, and you cited the example of colonoscopies and AI identifying suspicious lesions and then gastroenterologists not becoming, as you know, as accurate as they were before without AI. And that's a concern too. Although there are other cases where technologies have sort of gone beyond what traditional skills may have been. For example, before brain imaging, neurologists, the neurological exam neurologists could do exquisitely accurate, fine tuned exams that could take hours and they could generally pinpoint the lesion from very, very subtle clinical findings. In the age of mri, the need for that has gone away. It's an elegant form of medical physical exam and reasoning, but it just doesn't have much of a place when we can get such accurate imaging or at least not the same place that it did 40, 50 years ago before we had MRIs. So I think there's some evolutionary process here where, yes, there are some things that the AI is going to take care of and take care of in when it, once it's trained better than humans will. But until we get to that point, we definitely need to be careful about moving too quickly away from the knowledge and skill set that has enabled us to get to the point where we are.

C (26:59)

The second claim that I want to interrogate is AI will help us design new wonder drugs. I feel like every few weeks I read a headline that claims that AI has designed some drug to combat a superbug or be the next super penicillin or maybe even take on certain cancers.

B (27:17)

I would love you to tell me, as concretely as you can, how are scientists using artificial intelligence to design drugs.

C (27:24)

That they wouldn't be able to design without AI?

B (27:28)

And maybe another way to get at.

C (27:30)

This question, if it unlocks something else.

B (27:31)

Is if AI is a tool in the drug discovery process, what is that tool good at? That's new, right?

D (27:41)

Well, one thing it's good at today is it, you know, DeepMind, which is now part of Google, a number of years ago, published a large language model or made made available a large language model that can predict and calculate the structure of any protein by knowing its amino acid sequence. And it turns out to be amazingly accurate in, in doing that. Okay. If you're designing a protein, biological drug, protein based drug, and, and, and you know the structure of the receptor, you know the structure of your protein, but you want to manipulate the structure of your protein just a little bit, then you can, with the simulation, move amino acids around and look at how the conformation, the structure of the protein changes and make a prediction, then, oh, this is probably the molecule I want. This is the protein I want. That has to be experimentally validated. But before you'd be doing a lot of guesswork, you could do some calculations based upon the, you know, the charge of the charges of the amino acid, how they interact with one another. But the model is so much better than any, any calculations that were made before alpha fold was introduced. Now we're seeing a similar approach being used for nucleic acid structure and function. So today, has this, has the application of AI to processes of drug discovery massively changed the drug discovery process? No, it hasn't. A lot of the approaches that are being used today by the many companies that are deploying AI is to get as much biological information as you can. So if you're working on the liver, get slide after slide after slide of liver disease and pathology in a variety of different situations, all very, very well annotated, and then train your models based upon that biological data that you have absolute confidence in, and then start to do manipulations and make inferences from a database that is as pure as you can get for the need that you have in your drug discovery process. That's a logical approach. It's also, in this approach, the AI process is actually not the most difficult part of the, of the process. Most difficult part is getting all the biological data in to train the model as well as you can. And that takes time. And so again, it gets back to the statement, we tend to overestimate what we can accomplish in a short period of time, underestimate a long period of time. I think on a scale of five to 10 years, that AI is going to dramatically change drug discovery processes. But it's proving to be a longer timescale than I think some people had thought when it was first introduced. We first started seeing companies focused on this, say, five years ago, four or five years ago. So I remember.

B (30:55)

Let me jump in there because once.

C (30:56)

Again, I don't want to just repeat this over and over again, but I come at this wanting this technology to work. Who doesn't want AI to figure out how to design certain proteins or certain immunity blockers that ends up being absolutely fantastic for helping to cure certain diseases.

B (31:13)

But I do think it has been.

C (31:15)

What, four years now since that alphafold result was published? I believe in the Nature Journal. And there were some people saying, look.

B (31:24)

This is going to revolutionize drug discovery immediately. It's been four years, and to my knowledge, there is no drug that exists.

C (31:32)

That patients can take that is so called AI discovered. Right.

B (31:37)

We were mostly in the preclinical phase.

C (31:41)

Of working out these drugs.

B (31:45)

I don't want to necessarily say it's been disappointing, but maybe unpack a little bit more about what we've learned about why it's taken a little bit longer.

C (31:55)

Than we thought to use this technology to design drugs that help people.

D (32:01)

Right. And Derek, when the human genome, the first map of the human genome was published out 25 years ago, it was like, ah, we've just cracked all of disease. And look, we have made tremendous progress because of that initial work. And of course now genome sequencing is available at a relatively low cost. However, it's proven to be much more difficult to actually intervene and treat or prevent disease just from genome sequence information. I think there's an analogous situation what we're seeing in AI enabled drug discovery. It for sure can be used to design molecules, it can be used to curate biological information. But by the way, and you mentioned the fact that as of today there's no AI generated drug therapeutic that's gone all the way through the process, FDA approval and is on the shelf in pharmacies across America, across the world. And there are lots of reasons for that. There are oftentimes off target effects. You could have what seems like a perfect drug. And by the way, this happened long before AI. And then there's an off target effect. If it's a small molecule that you never expected, you didn't really have a reason to expect it, but it showed up and, and it delays or in some cases cancels an entire program. So it, it's a tough business. And I don't mean that as an excuse but, but also I, I'm, I'm optimistic that in the end the drug discovery process, we're going to have more effective therapeutics at a broader array of targets than we've had with the conventional approaches in the past. I think though, that's, we're really looking at before we see that sort of transformation. We're probably looking more at the 3, 5 and 10 year timeline than we are. And there were people that were hopeful it would be much faster than that, but it's proving to take a lot longer.

B (34:05)

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F (35:15)

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B (35:38)

Learn more@WhatsApp.com I wonder if what we're.

C (35:42)

Missing with AI and drugs is a new platform technology.

B (35:47)

So with diagnosis we have it. The large language models can memorize millions of case studies, it can formulate a theory of symptom and disease, and it can articulate its answers to patients and doctors in words. So the interface of large language models like ChatGPT is perfect for diagnosing patients who want to know why they're sick. But if the interface, if you will, for drug discovery isn't complete, that seems key here. Like let's say you design a drug that blocks a key protein, okay, well what knock on effects is that drug gonna have throughout the body? We have no idea. We have no idea whatsoever. We have no platform technology to give us that answer. Is there a missing invention that we need with artificial intelligence and medicine that we're just missing right now?

D (36:45)

Well, let's take one specific example, off target effects, which are oftentimes the reason that small molecule studies fail. And we can see that with biologicals, with protein based therapeutics. Also, if we had knowledge of all the receptors of all the cells and the interactions of all the receptors of all the cells and the detailed metabolomics of every cell, then we might be able to do a, we should be able to do a much better job of predicting off target effects. We have some knowledge today, particularly with regard to mechanisms of liver metabolism, as to when diseases are likely to be toxic, when therapeutics are likely to be toxic to the liver. But, but there are many other cases where we simply don't have the detailed knowledge or the assimilated knowledge of all the metabolomics of cells to be able to make predictions of, of these off target effects. So I think that that's a data acquisition and curation issue. But that would, that would certainly be of great utility in, in the AI deployment for, for drug discovery as we get better at simulating the, the metabolic functions of cells and then we can computationally calculate how interfering with one particular pathway is going to affect the overall physiology of the cell. Already that's being done, but that will undoubtedly be done more with greater and greater sophistication as our knowledge of the pathways and our simulation of those pathways increases. So I mean, those are a few examples of where I think, you know, the, where I think additional studies will help AI be even more impactful in drug discovery.

C (38:48)

But it sounds to me, just from my own edification, that like you're saying.

B (38:51)

The breakthrough here is that we've digitized.

C (38:53)

By Google an understanding of protein construction and we've digitized the design of certain molecules, but we haven't digitized a map of the effect those molecules would have throughout the body and throughout tissues in the body. And so it's sort of like if drug discovery is like a maze.

B (39:12)

It's like we found where the opening.

C (39:14)

Of the maze is, but we haven't actually created like a digital record of what the actual maze looks like inside the body. And so we invent these molecules and it's great and it seems like it's going to work exactly on the target, but we have no idea once we launch that, once we launch that, that pinball, what it's going to do inside of the body. That kind of makes sense to me.

D (39:35)

I think that's a great analogy, great metaphor, and it demonstrates the complexity of all these processes.

B (39:44)

Right.

D (39:45)

All right.

B (39:45)

Claim number three is that AI will accelerate clinical trials which will allow us.

C (39:50)

To test drugs faster and cheaper.

B (39:54)

This might sound a little bit nerdy to some listeners. If you're in the medical community, you.

C (39:59)

Know how unbelievably essential this is. Drug development has gotten exponentially more expensive in the last few decades. In the Reporting for Abundance, I spoke to one researcher at the Broad Institute who told me that on a per patient basis, the cost of making an.

B (40:14)

FDA approved drug has increased by 10x.

C (40:18)

In the last 20 years.

B (40:20)

What makes you most optimistic that AI could accelerate clinical trials?

C (40:27)

That we could test drugs faster and cheaper?

D (40:31)

Several things. First, large language models today are enabling us to identify patients who qualify for a clinical trial. For example, we here at Stanford have developed our own large language model based upon others that are available commercially but focused on looking through medical records in order to call out patients that may be eligible for a new clinical trial that we're just bringing online previously. How has this been done? How have we reached out or identified patients who may be eligible for any, Whether it's a phase one, two or three clinical trial, that is, whether it's a, it's a, a brand new drug or it's a drug that's in the last stages before FDA approval. The way we've done that is generally a physician who takes care of patients with diseases for which that drug is applicable, goes through their data and says, oh, I, I have this patient I saw, you know, a few months ago and, and maybe there's, there's a record of, of some of the mutations in a particular tumor, if we're talking about a cancer. But it's been very, very ad hoc and that's why it's been difficult to get patients enrolled. The other thing is just more sophistication at monitoring the progress of clinical trials so that we can adapt, you know, so called adaptive clinical trial design, which is rather than saying we are going to enroll 1600 patients in this phase 3 trials and then we will break the code and decide whether or not the therapeutic has been effective. Now, pharmaceutical companies and others conducting these large trials are able to set up criteria to monitor in real time and adjust, make adjustments in the trial, again based upon statistically valid methodology, adjustments in the trial in real time, enabling the trial to be much more efficient and effective at the end at knowing if the drug is effective and if so, at what doses is effective for what categories of patients.

C (42:39)

So with claim one, AI diagnosing diseases, I think we can say, yes, this is happening with claim two, AI designing new drugs.

B (42:47)

Yes, it's certainly trying to design new.

C (42:48)

Drugs, but we don't yet have a drug that's AI designed that someone can, you know, go to their doctor and be prescribed.

B (42:54)

To what extent with claim 3 of AI accelerating clinical trials, is this happening? Is it kind of happening in that.

C (43:01)

We know that AI is being brought.

B (43:03)

To bear on clinical trials, but we.

C (43:05)

Don'T yet know if it's making the trials faster or cheaper.

B (43:08)

What is the level of reality of.

C (43:11)

Claim 3 compared to claims 1 and 2?

D (43:15)

I think the level of reality is it's being deployed today. We don't yet know whether or not it will have incremental impact or transformational impact. I think it will take, I Think it'll take a couple of years to know that. But if we're predicting, and believe me, I don't have a crystal ball for any of this, and I've learned that many, many times in my career. But if we're predicting that the full impact of AI on drug discovery is going to be on a 3, 5, 10 year timescale, I think the full impact of AI on clinical trials is probably a 2 to 5 year timescale. That is to know, is this going to be a total game changer in terms of extending the efficacy and the prevalence of clinical trials, or is this going to be an incremental advance? I think we'll have a pretty clear idea in the next two to five years.

B (44:08)

All right, that brings us to claim four. This is the claim that AI combined with wearables will be a key weapon.

C (44:14)

In the fight against chronic illness. So, speaking personally, I've got an OURA ring. I'm wearing it right now. It tells me heart rate variability, sleep time, body temperature, all of that. Apple Watch has done studies showing that it can detect AFIB and potentially therefore be a warning signal for heart issues. So these devices are telling us how.

B (44:32)

Long we sleep, right?

C (44:33)

Body temperature, heart rate variability.

B (44:36)

It's not hard for me to just.

C (44:38)

Look up my HRV, which was I think 55 yesterday, and determine if that's good or bad for someone my age.

B (44:45)

So I wonder here, what can AI plausibly give, give us that these devices.

C (44:52)

Aren'T already telling us?

B (44:54)

Like, what more does AI theoretically bring to the picture of wearable individual data.

C (45:02)

And helping us stay healthier for longer?

D (45:06)

It's a great question. It's perhaps the area where it's most complicated because as you point out, we're already able to get a lot of information about the function of our body through devices that are available off the shelf today. I think where AI offers the most, will offer the most benefit is in knowing how much of that. Let's put it this way, we're getting a lot of data from those devices. Now, how do we derive information from that data? What? Okay, so we're showing a larger heart rate variability with exercise. Is that significant? Or maybe this just reflect that the person is fatigued, their overall physiological cardiac function is not as efficient as it was because of fatigue or other factors. I think that's where AI will be helpful, is making sense out of all the data that we get. I think it's great we're getting all the data. One of my late colleagues, Sam Gambier, late Chair of radiology here at Stanford, used to remark, you know, every time we fly on a, on a, on a commercial airliner, those jet engines are being monitored hundreds of times a minute with data being beamed back down to earth. And routine maintenance is being done based upon real time information coming from the engines. It's enabled them, you know, it's enabled airlines to reduce the, you know, the incidence of catastrophic engine problems and made their, their maintenance much more efficient. We don't do anything like that for the human body and maybe we shouldn't for everyone. But for people who are, who have multiple medical problems, people with congestive heart failure who are constantly teetering on the brink of being either fluid overloaded or fluid depleted, well, it would be nice to have a lot more information and be able to intervene proactively and keep people healthy at home rather than constant trips to the emergency department. But still, and probably there we have the best developed algorithms. But with general health, knowing the significance of a myriad of different findings and changes that we're picking up from these devices is hard to do today. And AI should help us determine that significance.

B (47:30)

So about three weeks ago, I got.

C (47:32)

My first blood test in, I won't.

B (47:34)

Even say how long, too long.

C (47:36)

I get the blood test back and.

B (47:38)

The website where I'm reading the test results, it's showing me my number for certain indicators like ldl, bad cholesterol, and.

C (47:49)

A bunch of other words and acronyms that I don't understand that tell me about my body.

B (47:54)

And it says the number and then it gives me this range, this healthy range, in which my number either is falling inside the healthy range or falling.

C (48:01)

Outside the healthy range.

B (48:02)

When I got this panel back via email, it just looked like a mess. I didn't know what any of it actually meant. So what I did was I took a screenshot of like maybe eight screenshots of every single part of this panel.

C (48:17)

I uploaded those screenshots to ChatGPT and.

B (48:20)

I just entered my information. I said, I'm a 39 year old man, I just got these blood tests back. Tell me what this means and tell me in particular if there's anything here that should strike me as very worrisome or on the opposite, like, very encouraging. And I just got this brilliant readout of my blood panel. And you know, someone might significantly ask Derek, how do you know it's brilliant? You're not a doctor? Well, my actual doctor called me 24 hours later and I swear to God.

C (48:49)

She gave me the exact same information.

B (48:51)

With the exact same context. That ChatGPT had given me in 15 seconds. What does that experience tell us about the future of artificial intelligence as a new or more sophisticated layer between patients and medicine?

D (49:09)

Oh, I think they indicate very positive things about the future of our use of AI medicine. When we started our conversation today, I mentioned earlier in my career describing an inner ear disorder. It took a while for other specialists in the field to get to know it, but patients had access to the information instantly through the Internet and started diagnosing themselves. So this is. And of course the advantage of AI is it takes it to a whole next level because you can give it a complex series of data points and ask it to assimilate those into some sort of a reasoned analysis of your risk factors. Or is this significant or isn't it? And so yes, I think it's going to, it's going to do all of those things help, help bring knowledge to our fingertips. People who have no training in medicine can now understand things that previously it would have been difficult to have deciphered or it would have taken a lot more time and effort to understand or put together the significance. And now it's available for you with a simple search.

B (50:19)

Last question.

C (50:21)

I'm quite hopeful about AI's effects in medicine in the long run. As you said, I think the short run is going to be difficult. I think medicine is just incredibly hard and understanding new tools and how they can optimally work in medicine is just going to require a little bit of a learning process. Where I'm worried about artificial intelligence and this connects, I think, to health, is that I think we live in a time where people are disconnected from each other, that loneliness is rising and alone time is rising and screen time is rising. And in many cases artificial intelligence is not necessarily going to help this. There's going to be people who don't go out with friends because they're staying home looking at TikTok, which is an AI algorithm. They're not going to to see a therapist because they say I've got Dr. GPT that I can talk to.

B (51:06)

Maybe they don't see a PCP because they're like why do I need a pcp? I've got my oura ring and we're learning more.

C (51:13)

And I've done recent episodes about the.

B (51:15)

Degree to which social connection isn't just.

C (51:18)

This woo woo thing that mom said was good for you, like you should make friends. Social connection seems quite protective neurologically. People who are superagers and retain fantastic memory at 80 seem to one thing that correlates with superagers seems to be their high levels of social connection. There's all sorts of research that seems to indicate that connection seems to be cardiovascularly and metabolically good for us. Being around other people is good for our bodies.

B (51:45)

And I wonder what your thoughts are.

C (51:47)

About this world of artificial intelligence that we're moving into that might keep us more trapped with digital devices and digital selves, which in many cases is what AI sometimes seems to us to be, and the health risks of that, and how we can balance both the best of AI while not giving into the worst of it by allowing it to accentuate our aloneness crisis.

D (52:12)

Derek I think it's a real concern, of course, that is the isolation and disconnectedness. But it begins, of course, long before AI. I mean, that's a complaint commonly stated with regard to social media in general that, that, that people can spend all their time, you know, interacting with tick tock or, or, or watching videos online, whatever. Whereas before, in order to have that sort of connectedness, you had to be sitting in front of someone or a group of people. So it, it's a challenge then to, to know, well, how. I don't think we turn, we never turn the clocks back on technology, but how do we responsibly use the technology? Or maybe think of ways that AI actually helps people to maintain more connectedness rather than less connectedness. Because AI identifies again without breaking, you know, without violating privacy. AI identifies connections with others in ways that might not have previously been known. Or there are discussions around AI that bring people together in order to have those meaningful discussions that wouldn't have occurred had there not been an advance in AI that provided a reason for the discussion. But for sure, the loneliness problem is a huge issue and it is growing, not declining. And like you were saying, there's great data that a variety of different health indices are improved by social connectedness.

C (53:52)

Dean Lloyd Minor, thank you very much.

D (53:54)

Thank you. It's been wonderful being with you today, Derek.

B (53:58)

Thank you for listening. Plain English is produced by Devin Beroldi and we are back to our twice a week schedule. We'll talk to you soon.

G (54:38)

And Doug, here we have the limu emu in its natural habitat helping people customize their car insurance and save hundreds with Liberty Mutual. Fascinating. It's accompanied by his natural ally, Doug.

B (54:54)

Uh, limu is that guy with the binoculars watching us.

G (54:57)

Cut the camera. They see us.

B (54:58)

Only pay for what you need@libertymutual.com Liberty Liberty Liberty Liberty Savings Ferry unwritten by Liberty Mutual Insurance Company and affiliates Excludes Massachusetts.