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Dr. Samantha Yamin (7:33)

As healthcare systems continue to be overburdened, it's not surprising people are optimistic about how AI might be able to help. In fact, 80% of physicians reported that they use AI in a professional context, according to a survey published in March 2026 by the American Medical Association. That's up more than double from 2023 but when people's lives are at stake, of course we have to be super careful about accuracy and maintaining trust between patients and providers. So it's a really careful walk along the line between hype and hope. To help us understand where things are headed. We're chatting with Dr. Nina Kotler, a radiologist and chief medical AI officer for Mosaic Clinical Technologies. She's also an associate fellow at the Stanford center for Artificial Intelligence in Medicine and Imaging. Welcome to the show, Nina.

Dr. Nina Kotler (8:21)

Thanks, Sam. Great to be here.

Dr. Samantha Yamin (8:24)

I'm wondering if you can start us off with the broad categories of AI use in healthcare and what kind of problems they're trying to address in new ways.

Dr. Nina Kotler (8:33)

At a high level, AI in healthcare is using data, and in radiology, that data is medical images like CT scans and X rays and ultrasounds. Takes in also reports and patient information to help clinicians make better decisions. That's just very high level in radiology. I don't know how many people in the audience know specifically what a radiologist does, because I think there's a lot of confusion. Sometimes I ask people, hey, what do you think a radiologist does? And they tell me, although you're the one that takes the images, when I go to get my hand X rayed, that's not actually the case. That's the rad tech. A radiologist is the doctor that interprets the those images. And in general, what we tend to do, and the reason why I went into radiology is because we're called the clinicians. Clinicians. We're the doctor's doctor. And that means we provide a consultation to the other physicians to help them understand what's going on with their patients. To do that, we need information. And that information, generally, for us at least to start, is a lot of the information in the medical images. And as we start applying AI, we're applying AI to that data to start to understand more from those images in combination with the patient history and all of the other data that is available about the patient within all of our electronic medical records. Radiology is one of the earliest spaces, actually the earliest space that AI came out in healthcare, and that was back in 2016. And that's because radiology is so very digital, meaning all of our information is not able to in those physical paper images anymore. Right. They used to be printed out images. Now they're all digital, so we can send them to different places. And that enables us to use AI a lot more easily.

Dr. Samantha Yamin (10:28)

And in that American Medical association survey from this year, 2026, there was a line saying, that the physicians were expecting the greatest benefits of AI in healthcare to be related to diagnostic ability and also work efficiency. And then there's like hospital admin and all the different healthcare worker workflows. Is that kind of all the different areas where we would probably see AI touching?

Dr. Nina Kotler (10:53)

Yeah, I think we started off, it's evolved. We started with AI and healthcare in 2016, and back then it was mostly diagnostics. It was a very narrow system that would help tell you if one thing was present or absent in a whole imaging exam. And now we're moving to another phase where we're trying to move more toward workflow. Workflow is a little different. There's so many things that we do that are not diagnostics, and that part is actually way harder. So when we concentrate on those workflow items, we're actually improving the efficiency from which we can get things done. And right now, the biggest problem in healthcare, number one problem, is actually not the quality. The number one problem is having enough physicians to manage all of the people and the exams that are needed to be interpreted. Because if you can't get to that, it doesn't matter how good your quality is. So right now, workflow is the biggest thing that can enable additional capacity in the system. But the be all, end, all the benefit in the end is figuring out what new opportunities are there that we can identify with AI. So instead of identifying disease after it's happened, how can we predict disease before it occurs? How can we, once we do identify disease, how can we determine what the best treatment and exactly what that disease is and how it's going to react to the different treatments we can give? How can we provide this information to physicians and to patients so that they can actually see what their body is doing and as they make changes in their lifestyle, how that's actually affecting them, Those are future things that we need to move to. We just have to get through this capacity piece in order to manage all of the patient care that we need. But it's not the end point. The endpoint is the improvement in healthcare.

Dr. Samantha Yamin (12:40)

Kind of going for the most urgent thing, the most immediate need, and the lowest hanging fruit is solving the capacity issue. Then we get into all the other more innovative stuff once we've caught up.

Dr. Nina Kotler (12:52)

Yeah, I don't know if people realize just how far behind we are with our capacity. This is physician capacity. And I'll give you the numbers for radiology, since I know that, but it is not limited to radiology. We are probably about 15% understaffed in radiology. In terms of the imaging volume compared to the number of radiologists that there are, and I was in London a couple of weeks ago, they're 30% double what we are. And the reason behind that is every year in the US we are ordering about 4% more imaging exams every single year. Now each image exam is also getting more complex, like there could be, and I don't know if people realize this, there could be a thousand images in a single imaging exam that we're interpreting. So about 4% more a year. And the number of radiologists that is increasing, it's 0.4%. Now, those numbers are both really small, so they don't sound like they have a big impact, but they're a 10x difference. So if every year there's a 10x difference in the amount of imaging versus the amount of people you have to manage that imaging, like just imagine how big that gap is. And in 2022, that gap crossed the threshold where we can't manage it anymore. So every year since 2022, turnaround times have been increasing, which means how long you have to wait to get your images before you're seen, how long you sit in the er. All of these things are increasing. And this is not just in radiology, this is in every specialty.

Dr. Samantha Yamin (14:15)

And I'm based in Canada where capacity issues are huge and wait times are really, really long. So I can only imagine what our stats are. What's interesting as we're speaking about capacity though is that I'm reminded of back in 2016, I remember Nobel Prize laureate and godfather of a, he's often called Geoffrey Hinton, said people should stop training radiologists now. And it is just completely obvious that within five years deep learning will do better than radiologists. That was a direct quote. So I'm curious, that doesn't seem to be the case today, but where has it fallen short and why didn't that pan out?

Dr. Nina Kotler (14:53)

Yeah, super interesting actually. That prognosis from him in 2015 actually decreased the number of radiologists that have gone into radiology. So that's worse than the problem and we are at the opposite end. I've been radiology for 20 years, I have never seen this big of a deficit ever. And so yes, this is a massive problem. Why did he guess that and where did he go wrong? I think people don't realize the kind of AI that you all might be using in your day to day. Maybe you're using Gemini or ChatGPT or Claude or Grok, any of these tools. These tools are called foundation models, meaning they're very general. They don't just answer one question, they answer any question that you could have. And they could do it with all different kinds of mechanisms. They could look at images, you could put in a copied image and it could interpret that, you could speak to it. Which speaking now generally is writing. So it can do language and images. They call that multimodal. What are we using in radiology? There was actually a really funny skit that if anyone has seen the TV show Silicon Valley, there's a Silicon Valley show. It was an episode from years ago where one of the guys in Silicon Valley created an AI tool. And the episode is hysterical. They talk about this tool, how it can identify what's in the image. Now the first thing they do is they take a picture of a hot dog and they run it against the AI and it goes, hot dog. And they're like, oh my God, this is amazing. It's going to change the world. Now remember, this was years and years ago. Change the world. This is fantastic. Then they say, do another one, do another one. So they take a picture of a pizza and they're all like, okay, what's going to happen? And it says, not hot dog. And that's actually where we are in radiology AI right now. It's very easy to just assume that we're as far ahead as where everyone else is using it today. But there's regulation, there's systems that don't really connect together. It's so much more difficult to deploy that kind of thing in the healthcare system. So right now we're at what we call narrow AI. It's a hot dog, not hot dog model. But instead of identifying hot dog, it identifies things like blood in the brain or a hole in the lung or cancer. And those are all really important, but they're not how I work as a radiologist or how any other human works. When I get a result from an AI that says there's a hot dog or there's some blood in the brain, great. I'm looking at maybe 500 images on a head CT. I want to describe where the blood is, if it's new or not. Is it getting worse than it was before? What associated findings are there? And then I look for 500 other things on that study, not just the one. So it's just very different from the experience that you might be experiencing in using AI in your day to day life.

Dr. Samantha Yamin (17:46)

Where do you see the biggest near win for AI? How would it make your, your day in that part of your workflow faster? Or more efficient?

Dr. Nina Kotler (17:54)

Yes, really important. Radiologists or any physician spends the least amount of time picking up things in the image. Why? It's the fastest thing we do. It's part of our cipital lobe back here in the back of our brain, and that has evolved over millions of years. We are actually really good as humans at picking up things. So when you have someone else that's looking, it helps improve the sensitivity of what we have. We pick up more stuff. But does it help us do it faster? Not really. We spend way more of our time doing other things in the workflow rather than picking up the pathology. So when we talk about workflow, like, what does that actually mean? Well, it's doing the things that are low value for us as radiologists. Super high value for us to be looking at the images, because that's where the patient is. Super low value for us to be editing the report and looking at the report. Especially if you're going back and forth, because humans are not good at changing our focus.

Dr. Samantha Yamin (18:50)

If I can quickly ask you almost the opposite question. Is there something that you don't think that AI will be able to help with in the near future? Of course, we can't predict long term. That maybe would surprise people.

Dr. Nina Kotler (19:04)

So, yes, I can tell you where we're starting to use AI in a way that it acts like a second set of eyes that it can do. It's very good at that. What it doesn't do is take accountability for the patient. I mean, ultimately, we as physicians take accountability for every single patient that we see. AI, we tend to anthropomorphize it to say it's acting like a human. We can only imagine what a human does. It's copying some of the things a human does. So we think it's being like a human. It doesn't have the same level of accountability. That's one thing. The second piece is a lot of things in health care these days are uncommon. When you look overall at a whole population, how many people have blood in their brain? Well, most of what I see, even for the patients that come into the hospital, so they're sick and we expect they might have something, it's about 5% of the time. So a lot of things in healthcare are uncommon. The less common something gets, the more times the AI will provide a result that's incorrect. And it's just the math behind it. It's related to what we call disease prevalence, which is how common something is, and what we call a metric of the AI, which is positive predictive value when it tells you there's a positive finding, how often is it right? And actually that gets really low when things are uncommon. So it's very hard for that long tail of findings for the AI to provide a right result. You need a second person and a human to look at that, to get rid of of the times it's wrong, all of the false positives. So AI produces a lot of incorrect results. The physician with the AI together that make the better combination.

Dr. Samantha Yamin (20:52)

I'm really interested in the evaluation side of AI, and I know you recently published about a new approach to evaluate AI models and their potential value in radiology. So I'm wondering if you can tell us about it and some takeaways on how to think critically about the positive impact of AI here.

Dr. Nina Kotler (21:10)

Yeah, I think a lot of people just immediately think if you look at the AI accuracy, especially what the vendor might give or what you see in the fda, because anything that involves an image medical imaging has to go through the FDA before it can be marketed or sold. And with that, the FDA will provide statistics that help determine if something is safe. And we looked at those statistics a while ago, both what the FDA provided and what the vendor provided. And it tells you things about how sensitive it is to finding disease, how specific it is to not over calling disease and a few other things, but as a clinician, as the end user, not a single one of those statistics is what I feel as an end user. And the biggest question to me, because these tools are not working on their own, because they're working with a physician, the biggest question is, how is the physician going to feel about using these tools? You could have the best AI tool in the world, the most accurate tool in the world, but if it is put into a workflow where the clinician is not going to be able to use it easily, then it doesn't matter how good that tool is. And all we're seeing right now, especially from the vendor, is like, look how good my tool is. What we need to do is look how well it's going to be accepted and utilized. And I have an example because we, before we roll anything out, we spend a lot of time educating our physicians, giving them expectations about how AI works, how this AI works, what to think like, how to work with it optimally. And we do that. In the beginning, we were doing this literally physician by physician on site. And we went to this one hospital, we said, hospital, do you want to look at the education that we're giving to our clinicians so you could see what we're doing to make sure that people are using the AI appropriately and really understand it. And they're like, oh great, yeah, let me look. So after that session they came to us and they said, oh, we bought three other AI systems that no one's using. Can you just go teach them how to use that too? And that tells the story. It's really true. You can't just rely on the AI accuracy, especially accuracy metrics that don't feel useful to us as clinicians. You have to think about what does it feel like for the end user, how could you provide them information and even predict in advance whether or not they're going to be able to use it. So the kinds of things we look at, we don't just look at sensitivity or specificity. We look at things like how much more effective will the physician be if we add this AI system?

Dr. Samantha Yamin (23:45)

AI is often framed as a way to democratize access. Bringing specialist level reads to rural or underserved hospitals help things where capacity might be most dire, maybe in under resourced areas where they have even more capacity areas. But does that play out when you think of the cost for some of the these technologies, the setup and expertise required, especially thinking about more remote areas where it might be harder to do these trainings for example.

Dr. Nina Kotler (24:12)

Yeah. In general, as technology improves, in most industries, as technology improves, cost comes down. As costs come down, you could use it more and more places. And that that allows us to take, in this case, take care of more patients. There's some things that are a little bit different right now because we're so early on and there is a cost associated with these tools. There's no hospital that I know of and no radiology practice that is just like bringing in tons and tons of money that they have. All of this money that they could spend on new tools like that would be great, but that just doesn't exist today. In fact, I know a lot of radiology practices are going out of business because reimbursement is coming down and the cost of care is going up. So it's just a difficult environment to be in. So right now if you have to add on an additional payment, like how do you do that? There's some groups that are saying, well, we're just going to invest in that and believe that we could recoup that dollar somehow, that it's going to improve patient care enough that there'll be less costs in the future because patients won't have to come back, they'll get the right treatment. The first I Wish we could say that everything was just about the quality of care we're giving. But as hospitals are losing money year after year, they also have to think about the amount of dollars that they're putting into the system. So it's a balance. And unfortunately, the government is not paying for these tools. The government pays for a lot of our health care. There's very little payment for AI. So the question is, how do we make this right? Because ultimately we can't just pay endless amounts to expect improved outcomes. We have to be able to do improve outcomes without increasing the cost of care. And I think ultimately that means about making the cost of care just more effective. And that's where we're trying to move to. It's slower because it's really hard to get these, these technologies into care. And most of the technology that we have today is that hot dog, not hot dog, which really doesn't make a big difference. But as we start moving to the foundation models that everyone's using today, those are much more valuable and capable. And I think they'll enable us to prove that we could pay for these tools and they will bring about a return on investment. And I'm sorry that as a physician, it pains me to talk about return on investment when we're talking about healthcare, because what we care about is pain, patient care. But ultimately we need to balance both.

Dr. Samantha Yamin (26:59)

Last thing I wanted to ask. AI can only be as good as the quality of data it's trained on. Are there any significant gaps in the training data at the moment, especially given how rare and uncommon you've mentioned a lot of diseases are?

Dr. Nina Kotler (27:14)

Yes. So this is difference between these narrow AI systems. Hot dog, not hot dog. And the foundation models, like the ChatGPT that you're using today at home, the narrow AI models are trained to find one thing and they tend to be trained on less data. The benefit of a foundation model is you just give it a lot of data, give it a lot of data, and you're not actually circling where things are telling it. The way you're doing is you're training it by having it help self learn, learn some of these things on its own. In radiology, we give the images and we give the radiology report and then the AI learns on its own. But because we're giving way more data, these tools end up being more, what we call generalizable. They work on a larger patient population. But the state of the art today, which is narrow AI models, you're exactly right, they're trained on a smaller data set. So if you have a piece of data that looks a little bit different and it's out of distribution from the data it was trained on, it won't work as well. And we know that that is the case. I think that as we start moving over to foundation models, that's going to be less of a problem because they're trained on just so much more data.

Dr. Samantha Yamin (28:23)

That's fascinating. Thank you, Nina, so much for being on our show and teaching us all about what we can expect in the hype, the warranted hype for AI in healthcare.

Dr. Nina Kotler (28:34)

I am very excited where we're going into healthcare. It's one of the only times where technology can have a massive impact.

Dr. Samantha Yamin (28:40)

That's Dr. Nina Kotler, a radiologist and chief medical AI officer for Mosaic Clinical Technologies. She's also an associate fellow and at the Stanford center for Artificial Intelligence in Medicine and Imaging.

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Dr. Samantha Yamin (31:03)

Be honest how easily are you influenced now? There may be a thing or two I've added to my shopping cart this past week after seeing them online, and I always check reviews before booking a hotel or choosing a restaurant the researchers at Dartmouth wanted to test just how much other people's opinions can sway us. They found that social cues can shift how we experience pain and how much mental effort we think something will take. Here's how they did it. 111 healthy adults completed three tasks. There was a task where heat was applied to their forearm and they'd have to rate how much it hurt, another task where they looked at videos of other people with painful grimaces. And the third task tested how much mental effort it took to rotate shapes in their head. Before each trial, participants saw a graph showing how 10 previous people had rated the upcoming task, but the data on the graph was fake. These were just random ratings not from other people and not actually related to the upcoming task. In this experiment, the graphs were the so called social cues. Participants saw these social cues, then gave their own ratings in all three tasks. The graph which served as the fake social cue could strongly shift both expectations and reported experiences. This persisted over multiple days of testing and 72 trials. There was this carryover effect where however they perceived one trial would directly bias the expectation of the very next trial. If the last one hurt, people expected the next one would too, regardless of what the social cue said. They also saw evidence for confirmation bias in the study. When an experience confirmed what the social cue had predicted, people readily adjusted how much pain or effort they anticipated feeling next time. But when reality contradicted the cue, the they largely ignored it and their next anticipation stayed anchored to what others had told them to feel rather than what they just actually felt. Some of the study participants were more influenced than others, but it was pretty consistent across domains experiencing the pain, witnessing someone else's pain, or having to do a task that requires mental effort. This research was published in the journal pnas. This confirms just how much social information can warp our perception in the moment and over time, others opinions can bias what we expect our expectations color what we actually feel, and what we feel feeds forward into what we expect next. The authors suggest this could help explain the placebo effect, the expected positive outcomes from a treatment understanding that could help set more practical treatment expectations in healthcare. But on a personal level, it's a pretty good reminder that just because someone else says something is hard or even painful, it doesn't mean your experience will be the same. Stay open to experiencing things differently for Warner Bros. Discovery Curiosity Weekly is produced by the team at Wheelhouse DNA. The senior producer and editorial correspondent is Teresa Carey, our producer is Chiara Noni, our audio engineer is Nick Karisimi, and head of Production for Wheelhouse DNA is Cassie Berman. And I'm Dr. Samantha Yuin. Thanks for listening.

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