661. Can A.I. Save Your Life?

24:52We were just told in medical school we can't detect these forms of cardiovascular disease using this test. But we asked ourselves, could AI do exactly that? That's coming up after the break. I'm Stephen Dubner. This is the Freakonomics Radio Guide to Getting Better. If you haven't heard the earlier episodes in our series, they are sitting right behind this one in your podcast queue. And we will be right back.

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28:03Bob Wachter, who is chair of the Department of Medicine at the University of California, San Francisco, has been telling us that AI has recently been proven super helpful to health care providers by acting as a digital scribe and cutting down on other paperwork. So that's great. But how about some more ambitious uses of AI in healthcare? For that, we will go to Pierre Elias. He did research with Wachter at UCSF, took leave from medical school to work for an AI healthcare startup, then went back and got his medical degree in 2016. And what is Elias up to now? I'm a cardiologist at Columbia University.

28:41I'm an assistant professor in biomedical informatics, and I'm also the medical director for artificial intelligence for New York Presbyterian Hospital. Okay, that's the part I want to hear more about. What does that mean exactly to be medical director for artificial intelligence at a big urban hospital chain like that? So my center develops, validates, and deploys AI technologies to help us find patients with diseases so that we can take better care of them. We run the largest cardiovascular AI screening program in the country. How big is it? And is it just your organization and all its branches, or does it go beyond your organization?

29:14The majority of our work happens within our organization. This is eight hospital centers, 180 clinics in the greater New York area. But we really do try to make a lot of this work exist outside of those walls. I co-founded a consortia called Train Cardio, the Task Force for Research Advancement in AI and Cardiology. This is 20 plus institutions around the country where we regularly validate each other's work, collaborate on large projects, and when possible, freely share that information or that data with the world so that other people can build upon it. When you talk about building out this network of people like yourself, give an example of a particular type of project.

29:53A number of years ago, I got a call in the middle of the night from an outside hospital. And they said, we have a patient that we think we need to send to you urgently. This was a gentleman who had shown up three months before in their emergency department. And he had shown up with some chest tightness and shortness of breath. They checked him out. They ran a battery of tests. And they said, listen, you're not having a heart attack. We hear a murmur in your heart. You should go see a doctor about that, but you're fine. You should go home. He feels better. He goes home. He's waiting to see his primary care doctor.

30:25Two months go by, and he has an episode of the same sensation, this chest tightness and shortness of breath, and he goes back to that same emergency department. But this time, he's in respiratory distress. They end up having to send him to their intensive care unit. They have to intubate him and put him on a ventilator. And at that point, they do an ultrasound of his heart, and they see that he has severe valvular heart disease. We have four valves in our heart and like the plumbing at home, it can get rusty or leaky. And if it gets really severe, it can become life-threatening. Columbia is a world-leading center in valvular heart disease.

30:59And so they were calling me in the middle of the night saying, we really need to send this guy to you. I said, absolutely. He came and I spent the rest of the night working on him. But unfortunately by the morning, he was in multi-organ failure. He arrested and he passed away. This was a gentleman who otherwise had no past medical history, was otherwise healthy, and I will never forget having to sit down with his partner and say, I'm so sorry, there's nothing I can do for him. The thing I became convinced of was if we had just known about this disease, we would have been able to do something about it.

31:29He could have gotten a same-day outpatient procedure, and I think he'd be alive today. And it's hard to imagine that you couldn't have known about the disease when he had been A, in an emergency department at a different hospital, and then B, sent to you. So how rare or difficult to detect is this disease? This is the fundamental challenge in all of medicine, is you can't treat the patient you don't know about. Oftentimes, we're waiting until patients develop symptoms. But for many diseases, symptoms are a late presenting case. I became obsessed with this question, which is we don't have a screening test for the most common cause of death in the world, which is most forms of cardiovascular disease.

32:08And the reason for that's relatively straightforward. The way that we diagnose most forms of cardiovascular disease today is either too invasive or too expensive to do at a population level. Too invasive would be what? Cardiac catheterization, where we poke you in your arm or your groin, and then we shoot some dye into the vessels of the heart to take a look at them. How often is that performed on a healthy patient? Never. You would never do a cardiac catheterization on a healthy patient. Patients would be presenting with symptoms like chest pain or inability to exercise before you would consider doing a cardiac catheterization.

32:41Okay. And the too expensive option then would be what? It would be an echocardiogram, which is an ultrasound of the heart. An echocardiogram costs a few thousand dollars. It's an hour-long procedure where they look at the heart in a bunch of different angles. And so we end up in a situation where the way we diagnose cardiovascular cardiovascular disease is either too expensive or too invasive to do at a population level. So we wait until patients oftentimes present with symptoms, which is late in their disease course, and patients have worse outcomes because of that. Okay, so all you need to do is what then?

33:12All you need to do is magically find a way to create a cheap ubiquitous test that can screen for the most common cause of death in the world. And I became obsessed with this. I asked myself, well, is there anything that we're doing today that could fill that role? And what I came up with was the humble electrocardiogram. So if you've watched any medical drama and you see the squiggly lines in the background that go beep, beep, beep, that's an electrocardiogram where we measure the surface electrical activity of the heart. If you have an Apple Watch, you can do a one-lead electrocardiogram right now.

33:44We are taught from medical school onwards that you cannot diagnose those diseases with an electrocardiogram. It's simply taught as not possible. But we asked ourselves, could AI do exactly that? We built one of our first AI models, and we were shocked to find that it worked really well. We tested it on nearly 20 ,000 patients in a retrospective data set, and we found this AI model could outpredict me in trying to find valvular heart disease from the electrocardiogram. And then we asked ourselves, well, could we find all forms of structural heart disease from an electrocardiogram? And that set us off on this journey of the last five years where we built this technology called ECHO-NEXT, which looks at an electrocardiogram and tells us, does this patient have structural heart disease or not?

34:27Did you identify that relationship? Or is it just that the machine learning helped you find the cases in which one was related to the other? I didn't. Only AI did. And for a long time, we thought one of the holy grails would be, hey, you know, the AI would see something, it would tell us what it saw, we could teach it back to doctors, and then they would go and see that. But it doesn't work that way. Modern AI techniques still don't do a very good job of explaining what is it that the AI sees. And the AI does not think the way a doctor does. A doctor has a very specific way of interpreting this sort of medical data.

35:03And we've shown in a series of studies and experiments, the AI doesn't care about that. the AI is not thinking the way we think. It's doing its own thing and we can't fully explain what it's doing. But what we do know is it can see things that we can't and it can accurately predict patients who have structural heart disease much better than I can. We took 13 cardiologists from around the country and we had them look at 3 ,000 electrocardiograms. And then we asked AI for these electrocardiograms, does the patient have structural heart disease or not? So we asked a yes, no question. Random chance would be 50%.

35:40The cardiologists were at 64%. Oh boy, that's not very good. It's not very good. But you know, the cardiologists weren't surprised. We didn't think that the cardiologists would do very good. They didn't think they would do very good. Then we asked AI and it was 78%. The cardiologists were as far away from random chance as they were from the AI model in being able to predict which patients had structural heart disease or not from their electrocardiogram. Did you then use the two in tandem, the AI and the humans? We did. And the cardiologists got a little better, but they weren't as good as AI alone.

36:12The cardiologists with AI were 68%. What's funny is myself and my co-creator, Tim Boruca, we did the survey as well, but we recused ourselves from the results. And we were no better than the other cardiologists, even though we built the model. Wow. The really shocking finding, though, was half of the patients that the AI model thought were high risk for undiagnosed structural heart disease, don't go on to get an echocardiogram in the next year. So what that told us is there was smoking gun evidence that there was a lot of people out there with undiagnosed structural heart disease. And this is like clinically significant disease.

36:45This is the sort of stuff where if I told any doctor, I believe that a patient had heart failure or severe valvular heart disease, we would all agree that we need to clinically act upon this now. And that led to us running something called CACTUS. This is the largest cardiovascular AI screening trial in the country. It's happening in eight emergency departments in the greater New York area. Any patient who shows up in any one of these emergency departments, if they get an electrocardiogram, they're automatically being screened by the AI model for undiagnosed structural heart disease. One of the most striking was a young man who had been exposed to smoke during the LA fires and then had recently moved to New York.

37:26He had shown up with some shortness of breath in the emergency department, and they had presumed that this was asthma and bronchitis, and they had sent him home with an albederol inhaler saying, follow up if you need to, but nothing else is necessary. It turns out that Echonext told us this patient was very high risk for undiagnosed structural heart disease, such a high risk that we actually got the procedure done urgently. He had severe heart failure, and he was ultimately found to have a rare genetic mutation that put him at a one in four chance of dying before it would have been diagnosed. He ultimately underwent a heart transplant and is home with his family today.

38:04When you look at your colleagues, who's embracing AI or deep learning, as you sometimes call it? Who's ignoring it? Who's maybe actively opposing it and so on? Many people in healthcare, they don't have an AI reluctance. They have a technology reluctance that is a learned response from previous data. Meaning anytime someone comes with some new technology, it often makes their life harder. This new technology is supposed to help us optimize billing, but requires you to spend less time directly taking care of the patient. This is why I think it's really important for healthcare practitioners to be part of the process of creating this technology.

38:43Do you feel like you're sort of at the front edge of a movement? 1 ,000%. No one was talking about this, you know, seven or eight years ago when I started doing it. It's been 10 years of people saying, no, that's not possible, slamming the door on your face, and very much having to take a step out on the bridge while you're building it. The moments where I feel like I'm really doing science is when I genuinely do not know the answer to the question, but I know it's important to answer. And if it works, it would be fundamentally groundbreaking to the way we think about practicing medicine. Coming up after the break, as any new technology spreads, there are the inevitable winners and losers.

39:26The tech companies are playing this as we have no interest in replacing the doctor. We really want to be a co-pilot. We want to be your wingman. But they obviously do want to replace the doctor. I'm Stephen Dubner. This is the Freakonomics Radio Guide to Getting Better. We will be right back.

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41:56many companies are investing many billions of dollars to build out new ai infrastructure in healthcare for all sorts of applications clinical treatment and risk prediction drug discovery revenue and staffing operations on and on there's also one massive incumbent to consider the electronic health record company Epic, which claims to maintain at least one record for 325 million people. Here again is Bob Wachter, chair of the Department of Medicine at UCSF and the author of A Giant Leap, How AI is Transforming Healthcare and What That Means for Our Future. Epic won this market. This was a little tiny company started by Judy Faulkner in the basement of an apartment off the University of Wisconsin.

42:44She became the most successful female entrepreneur probably in American history. It's a really remarkable story. I think they won because they were the best. The best was partly integration. Judy's theory of the case was we're not going to bolt on 37 different tools by a bunch of different companies. We're going to own the entire thing, and that is going to allow us to provide an integrated solution And medicine is so complex and there's so many moving parts that if you don't have an integrated solution, the thing's not going to work very well. I do wonder how much of this kind of sclerotic nature of the electronic health record market and the attendant difficulty in using those data to move forward, as people have been trying to do in the past but not succeeding very much, how much of that is due to the fact that Epic already has a lot of success by playing the status quo and that they don't have much incentive maybe to innovate or to let others play with their data in a productive way?

43:44I think all of that is true, and generally monopolies are bad. I think it's going to become more true in the coming years because of AI than it has been true over the last 10 years. I don't think Epic is the main part of the problem up until now. And the reason I distinguish the past and now is once AI became a thing, so much of our data is in the form of narratives in a medical record, which until generative AI, you know, was really unstructured data that was not useful. You could analyze your hemoglobin and your creatinine and an EKG finding, but not my note, which might be a page long of narrative, and try to sick the old kind of AI on this is a 62-year-old man with a history of congestive heart failure who comes in with shortness of breath and chest pain.

44:38Impossible. The AI couldn't deal with that. So it was really not computable until recently. Now that it is, and now that generative AI creates the capacity for all sorts of magic, the idea that Epic is going to own the entire enterprise and you're going to need to use Epic-built tools for all of the different use cases of AI, and there are going to be hundreds, I think that's going to really slow things down. And the government is forcing Epic to become more open and more amenable to bolting on third-party tools. So who do you think will ultimately win the AI healthcare platform wars? Do you think it'll be the big incumbents like Google and Microsoft?

45:19Do you think it'll be the well-funded AI startups? Will it maybe be someone or something else? I was on Google's healthcare advisory board when Eric Schmidt came in and said, this is one of the biggest things that people search on. We're going to figure this out. And they started building a version of their own electronic health record. About a year and a half later, Eric came in and disbanded us. He said, this is too hard for us. And I said, wow, it's too hard for Google. That must be pretty hard. Google, Amazon, Apple, NVIDIA, Facebook, Microsoft all have designs on healthcare. They know how important it is.

45:52They know how big an industry it is. They all seem to screw it up time after time. It's not that they don't have enough smart people or enough resources. It is that so much of healthcare is local baseball. How payment works, how doctors think, how things are organized, they're just so far away from the day-to-day workflow. The real winner here probably is Epic, meaning that this advantage of incumbency, of having all the data in it already, and so far they are building AI tools for basically everything, they're probably not as good as a tool built by a third-party company that's focusing on this one use case.

46:29but the advantage of having an integrated tool and building tools that are good enough. And if I'm a hospital, I'm trying to decide, do I wait for Epic's tool? All I have to do is turn a switch and it's on. And I'm sure Epic's going to be in business in five years. Or do I buy a tool from this really cool new startup down south of market in San Francisco, but I'm not sure they're going to be in business. A healthcare organization like mine, their default setting is to buy it from Epic rather than to buy it from the third party. I assume that everyone in the world has tried or is trying to buy Epic?

47:01Everyone in the world has tried to buy Epic. And the answer from Judy Faulkner is now 82, is that it is not for sale. She does not accept any investment. Is there a next generation of Faulkner leadership? It's probably an internal person because the culture of the company is pretty insular. But they seem like they want to remain private for the foreseeable future. Not just seem in her will. It says that it must remain private. It goes to a consortium of current employees and her family and cannot be sold. And how do you feel about that? Judy had a partner in the early days, and the argument they had was over this.

47:38He said, we need to accept VC funding. We're not going to grow fast enough. And she argued that we need to own the entire process here if we're going to create this integrated system where all the pieces fit together. And he interviewed a couple of years ago. He said, obviously, she was right and I was wrong. They have been a massively successful company, and the product they produce is quite good. We use it, and I'm reasonably satisfied with it. I think over time, there's no way that a single company can produce the best AI tools for use cases that span the range from an AI scribe to an AI diagnostic support system to an AI tool that deals with the insurance company to an AI tool that facilitates clinical research.

48:22There's just no way that one company sitting on farmland 10 miles from Madison can possibly do that. Their ambition is to do that. I think the world is a better place if this is more open to third-party innovators bolting in, and that's going to require more federal push to do that because that is not in the company's DNA. So what do you see as the proper role of government in regulating AI and healthcare generally? Well, I think that's one. One is to create a level playing field and to be sure that innovators have a fair chance to succeed in an environment where there is a tendency toward monopoly, in part because of this idea that one company owns all your data.

49:02The issue of regulating AI tools, I have a chapter on it, and I came up with the very unsatisfying answer of this is really hard. And our existing structures, meaning the FDA or the Joint Commission, which currently accredits American hospitals, are not fit for purpose for this tool. The FDA can regulate a new radiology tool because they've regulated devices forever. They've regulated pacemakers and defibrillators, and they've regulated drugs. They can regulate a tool that gives a static answer, the same answer three years from now that it gives today, and whose implementation, the way we put it in at UCSF versus the way it's put in in a rural hospital 100 miles from here, probably isn't going to change all that much about the way atorvastatin works on your blood pressure or a CT scan reader works in giving you an accurate reading.

49:57But most of AI is not like that. Most of AI can shapeshift tomorrow compared to what it did today. Most of what it's doing is giving answers or insights based on the literature. How is that different than regulating a textbook or a medical journal? So I don't think we have even begun to think about what the regulatory infrastructure looks like to get this right. I think the Trump administration is going too far in an anti-regulatory way, but I do think the risk of over-regulating this in the short term is higher than the risk of under-regulating it. There are sufficient guardrails in the healthcare system, maybe with the exception of direct-to-consumer use of these tools.

50:38But in terms of my institution, a place like UCSF, is a$15 billion business that has a terrific reputation. It's surrounded by a whole lot of malpractice attorneys. We're going to be really careful about bringing an AI into our system until we're pretty darn sure that it's going to work and be effective and not hurt anybody. There are a lot of existing guardrails, along with just professional conservatism, that I think if you try to have government regulate every single decision support tool, that's impossible. There's no way they can keep up with the speed of innovation here. So I think we've got a lot of work to do to figure out the regulatory environment.

51:15But in the short term, I think a relatively light regulatory touch is the right way to go. So that's what Bob Wachter has been thinking about, how to lay out a plan for a full-on merger between AI and healthcare. And let's say that merger happens. What should we expect? What are some of the most wonderful benefits to us, the users of the healthcare system? I went back to Pierre Elias to ask about that. I see the ability for every patient to receive exceptional quality of care. And in a cost-effective and affordable way, we make sure that the right steps are being taken for each patient so that they're being effectively triaged, being screened for diseases the way they need to.

51:59And wherever there is uncertainty about what should happen next, we actually are able to quantify what that degree of uncertainty is and help patients navigate through that care journey without second guessing what is actually going on. How do you see the role of the physician changing? And I would assume this means that medical education should be changing quite a bit too, yes? I think the role of the physician continues to be that of the person on the journey with you. I think about all of the scariest and most important things I've done in my life, and I think about the people who were there to guide me through it.

52:41Those people leave indelible marks on you and help guide you through this challenging process. It may not be that mechanically they're having to do all of the work. It may be that the important thing is helping you reflect on what this journey means, helping find the places where there are lingering questions and uncertainty, and helping prepare you for what the next steps are going to be. I practice cardiology in a different way than I was trained to, because I believe we have to think about the way we practice medicine in new and novel ways, and AI is gonna allow us to do that. I think for now, it would be a mistake to take too much of clinical reasoning and the facts of medicine off the plates of trainees.

53:29That's Bob Wachter again. Because I think you could easily enter a death spiral where the AI is better than the doctor because the doctors are getting worse. You're talking about de-skilling now, yes? I'm talking about de-skilling. Is the de-skilling argument, which is that, you know, if physicians continue to rely on technologies like AI, they will lose the ability or the skill to actually do what they used to do. Is that an argument being made by the physician incumbency to scare off AI? Partly, but not completely. You know, there's good de-skilling and bad de-skilling. I will admit to you, I have de-skilled on map reading.

54:05I can no longer read a map. The question about de-skilling in medicine is complicated. There are parts of de-skilling, for example, the physical exam, we're definitely not as good at as we used to be. There are elders who lament that partly because the physical exam was about its clinical value and partly about the laying on of hands and sort of the connection between the doctor and the patient. But I think it gets romanticized. I would rather have a CAT scan than my lung exam to try to figure out what's going on in your lungs or your abdomen. So there are certain parts of de-skilling that just happen because the new technology is better than what we used to do, and you no longer need the technology.

54:49Last year, a study published in the Lancet Gastroenterology and Hepatology Journal, one of my favorite journals for light reading, looked at this question of potential de-skilling because of AI. Very experienced gastroenterologists who did this procedure called colonoscopy, looking up into people's colons, were given an AI colonoscopy tool, which puts a little box around lesions inside the colon that it deems suspicious. and find some things that the doctors will miss. They had access to the tool for three months. The doctors liked it and they benefit from it. Then the tool was turned off. Their performance on doing their colonoscopies fell significantly after the tool was turned off.

55:32These were doctors who had an average of 10 years of experience doing this procedure. So in just three months of exposure to this AI crutch, they got less good at this thing that they had been doing for 10 years. These questions of how the AI interacts with human actors in really complex systems where the stakes are high and the AI is getting better every minute and the humans are not, I think are really fascinating. Let's say that AI in healthcare delivery continues to improve and augment the lives of patients and physicians the way that you're describing in this book, the way that you hope it does.

56:07I'm curious what you see that looking like and especially how the role of the physician changes. We did an episode a few years ago about what are called cobots, robots that are collaborative. This was in nursing homes in Japan. They were these big physical robots that could help lift a patient clean and so on. And the finding from research around them was that the healthcare workers actually loved it, A, and B, were able to lean into what they as humans are good at, which is dealing with patients on a human level rather than just moving them around and getting them to the bathroom and stuff like that.

56:46So if we use that as the sort of model here, let's say for a physician like yourself or maybe someone a generation or two younger, if AI unfolds the way that you're hoping it does in 10 years, let's say, what does the physician get to do that maybe they're really great at now that some of the burden has been lifted by AI? I think the fundamental question of AI in healthcare is not creating my note or reviewing my chart. It is computerized decision support. It is the AI helping me make the best decision for you as a patient based on things about you, but also about the medical literature, which evolves, changes very, very quickly.

57:26And the best decision is not only the one that provides the best outcome, but the one that's the most cost effective. It's been said that the most expensive piece of technology in a healthcare system is the doctor's pen, which, of course, is no longer the pen. It's the keyboard. Where this really will have an impact is I'm seeing you in my office or in the hospital. And it's not like I now pull out my phone and say this is a 52-year-old man who comes in with this, this, and this. the AI is already reading your chart. It already knows all those things about you. And it's suggesting in real time, suggesting diagnoses and suggesting what the right tests would be and what the right treatments would be.

58:07Now, do you need me in that setting? I think so, but we'll have to see. I think you need me to interpret all of this, to be a tiebreaker when there's a tough call, to deal with some complex, sometimes ethical issues, to weigh your own preferences as a patient or family. There's a lot of complexity in this that I think goes beyond the kind of decision-making support that Waze gave me this morning when I drove into the studio. The tech companies are playing this as we have no interest in replacing the doctor. We really want to be a co-pilot. We want to be your wingman. But they obviously do want to replace the doctor.

58:45I think for the foreseeable future, the complexity of medicine, the stakes of medicine, and the regulatory environment. And periodically, I do have to say to a patient, you have Alzheimer's disease or you have cancer. I don't think patients are going to accept the idea that a bot's going to tell them that. Although you do write about the fact that AIs can do better in the empathy realm than humans. That was one of the shockers that in those early years, which is really only two years ago, when we saw AI passing the medical licensing board or doing well on really tough clinical cases, It was like, okay, it's pretty smart.

59:22And then studies began to come out saying if you did a blinded trial of a patient actor being given answers either by doctors or by AI, they often preferred the answers from AI and the AI appeared to be more empathic. Of course, the AI has no empathy, but it can fake it really, really well. Some of my doctor friends have been telling me that their patients will come to them having used chat or some other AI bot to go through some scenarios or symptoms or whatever. And the docs seem generally pretty happy about it. And this is to me in stark contrast to a trend from 10 or 20 years ago where direct advertisement began on television for pharmaceuticals where patients would come and say, okay, I just heard about this thing.

1:00:09I just need you to sign me up and give it to me. And they feel now, at least this is for my friends, that the AIs are becoming a pretty decent research tool for the patient to then bring to them the expert. I'm curious if you're seeing that, how you feel about that trend generally? Seeing a ton of it. I mean, they were doing a version of this with Google, but these tools are better. And so the answers they're getting are better. I think it's net positive. I think anything that democratizes healthcare is going to be good, assuming the answers are reasonable and correct, and assuming that patients, when they need to see a doctor, still see a doctor.

1:00:45And I think that's the open question here. Okay, Bob, here's what you've said that you hope AI can do. Produce better outcomes for patients, lower costs, and add some relief for beleaguered doctors and nurses. Then you say, however, that the success of all this will depend on history, politics, economics, pride, regulations, leadership, lawsuits, guilds, culture, workflows, inertia, greed, hubris, vibes, and zeitgeist, as much as biographical processing units, diffusion models, and neural networks. In other words, the tech can work, but then we get those layers of people who may feel that their realms are being infringed upon.

1:01:24So the way you describe it there, it sounds like what some people like to call a wicked problem, which is basically unsolvable because there are so many constituencies and so many of those constituencies have incentives that are at cross-purpose with the other constituencies. So when you take a look at the big picture, how much optimism do you have? Do you think that the upsides of this technology will be able to be successfully integrated into healthcare delivery itself? Or do you think that AI becomes yet another piece of the mess that is the U.S. healthcare system? I would interpret that very, very, very long sentence as saying it's not just about the power of the incumbents, although it's a very real part of it.

1:02:11It's about the complexity of medicine. It's about the regulatory environment, which for important reasons says there are certain things that we're going to restrict people's ability to do or require that a human does it rather than a bot. It just says that this technology can be really, really spiffy and still not deliver because so much of it depends on humans and their systems and their governance and their culture and their own self-interest. I hearken back to the old Yogi Berra-ism. in theory. There's no difference between theory and practice. In practice, there is. I think that's what we're going to see.

1:02:47In practice, that's where the rubber is going to hit the road here. I think it's going to be net very positive. Patients think that too. There was a Gallup survey last year when people were asked their attitudes about AI. They were really negative about its impact on jobs, on the political system. And I sort of feel the same way. The one area they felt positively about it was in medicine. I think that's partly because the AI is really good and partly because the system is so screwed up. Everybody recognizes that we are in desperate need of reform in healthcare. And our typical go-to response in medicine when we can't do what we need to do is we just hire more humans.

1:03:24A, we can't afford it. We're already 20 % of the GDP and bankrupting businesses and people and governments. But B, we can't even find the humans even if we could afford them, at least for the foreseeable future. This is going to help me do my job, help me be more of the doctor or the nurse I want to be, help me focus on the patient. So that leaves me optimistic over the next 10 years. Will there be jobs for doctors 20, 30 years from now? Well, unless I live to 120, that's no relevance to me. But, you know, I think there will be.

1:03:59That again was Robert Wachter, whose new book is called A Giant Leap. We also heard from Wachter's one-time mentee, Pierre Elias at Columbia University. My thanks to both of them. I learned many things in this episode. Hope you did too. I especially liked learning a little bit about Judy Faulkner and Epic. Now I'm hoping we can bring her on the show sometime. This is the final episode in our Guide to Getting Better series. Let us know what you thought. Our email is radio at Freakonomics.com, or you can leave a review on your podcast app. Also, if you want to keep up with everything we do around here, you can sign up for our newsletter at Freakonomics.com or at StephenDubner.Substack.com.

1:04:43Coming up next time on the show, for the Super Bowl, we will tell you why NFL running backs don't get paid the way they used to. And then in a new two-parter, we will look at what it really means to cheat. People like to call it cheating. You can call it that. I'm not sure who was cheated, but that's just what it was. If you won the Tour de France while doping, but everybody else was also doping, were you the cheater? And what would happen if there were no rules against doping? My goal is to bring about the 10th age of mankind, the enhanced age, where everyone has the opportunity to become enhanced.

1:05:22That's coming up soon. Until then, take care of yourself. And if you can, someone else too. Freakonomics Radio is produced by Stitcher and Renbud Radio. You can find our entire archive on any podcast app also at Freakonomics.com, where we publish transcripts and show notes. This episode was produced by Dalvin Abawaji and edited by Ellen Frankman. It was mixed by Jasmine Klinger with help from Jeremy Johnston. Special thanks to Rochelle Walensky for background research help. The Freakonomics Radio Network staff also includes Augusta Chapman, Eleanor Osborne, Elsa Hernandez, Gabriel Roth, Ilaria Montenacourt, Tao Jacobs, and Zach Lipinski.

1:05:58Our theme song is Mr. Fortune by The Hitchhikers, and our composer is Luis Guerra.

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