How AI Can Bring Humanity Back to Healthcare with Lloyd Minor

21:30Guy Kawasaki:No one is more bullish about the potential of AI than me, but, you know, it seems to me when I read these horror stories about there was a misdiagnosis or a hallucination, not just in medical, but in anything, you know, the comparison is always like AI made this mistake. It's as if humans never make mistakes, right? So the accurate comparison is apples against apples, oranges against oranges, right? So it has to be like, how many times is AI false positive or false negative? How many times are humans false negative or false positive? It can't be that humans are perfect and AI is imperfect, right?

22:09Guy Kawasaki:It's the relative amount, right?

22:11Dr. Lloyd Minor:And of course, the advantage of AI is that when they are fully ruled out and when the models are trained, as we know they're capable of being trained, the models will have been trained on orders of magnitude more images than any human being will ever see in their lifetime. And that's particularly important when in pathology, for example, there are rare tumors that a pathology expert, a human expert pathologist may see a dozen or so in their career. But if we're pooling images in training these models from a variety of different health systems and those images have been carefully interpreted and curated, then the model has the advantage of seeing a lot more than any one human can see.

22:59Guy Kawasaki:Also, Lloyd, it seems to me that you see multiple doctors, you have multiple prescriptions. Is somebody saying, all right, so his cardiologist recommended a statin, his ENT recommended a diuretic, his psychiatrist recommended lorazepram. Seems to me that there are infinite number of drug interactions that no human could keep track of. Oh my God, these are all the things that could happen. And wouldn't AI be perfect to bring to light that this is not a good combination?

23:32Dr. Lloyd Minor:Exactly. And that's going on today, looking for drug interactions, calculating the dosages of drugs. Guy, when I went to medical school, not only did we have to learn, meaning memorize, the names of drugs, their mechanisms of action, we had to memorize the dosing, the number of milligrams per kilogram that this particular drug was dosed. That's not good. The human brain is not set to store arbitrary facts in any reliable manner. Now, by and large, the dosages of drugs are calculated based upon the medical record knows the patient's height and weight, also knows the other medicines their own as long as it's kept up to date.

24:15Dr. Lloyd Minor:And the medical record, the prescribing record, will calculate the dose that's needed in that patient based upon other medications they're on. will also flag to the provider if the medicine they're trying to prescribe actually is contraindicated based on other medications that the patient is on. So this is going on today, and we're beginning to see significant impact in terms of reducing the number of medication errors.

24:40Guy Kawasaki:So I'm going to give you a real-life case study, and I just want your advice, not in the medical sense, but just as a practice. So I recently, I went to Kaiser, I got an x-ray, I read the results, I took the results and I pasted it into chat GPT. And I said, tell me what the hell this means. So I'm going to read you the diagnosis. I'm going to read you what chat GPT told me and tell me, is doing something like this guy, you're crazy. Don't do that. You don't know how to interpret this or guy, it's okay. So let me read you the report. And this is your specialty. So there's no excuses here, Lloyd.

25:16Guy Kawasaki:So it says, grade one, C3 and C5 retrolysthesis, whatever that is, normal vertebral body height, no acute fracture, multilevel disc space narrowing. Prevertebral soft tissues are normal. C1 and C2 are normally aligned on frontal view. you have mild backward slippage retrolysthesis of the C3 and C5 vertebrae relative to the ones below grade one means normal movement and the summary was in plain terms mild degenerative changes and slight misalignment not an emergency but possibly a source of neck stiffness or pain which is why I went in an orthopedic or spine specialist can confirm if physical therapy or posture correction is advisable.

26:07Guy Kawasaki:Is that okay? I don't know what to believe.

26:10Dr. Lloyd Minor:It sounds okay to me, but I'm not an orthopedic spine surgeon or a neurosurgeon. But based upon the report that you read, it sounds reasonable to me. I would certainly recommend and oftentimes ask this, and that is AI still is not, and I think it will be a long time if ever, that AI supplants the need for a human-to-human conversation about health. But that sounds like a reasonable synopsis to me, but I would check it with someone who's a real expert in the area.

26:40Guy Kawasaki:I'm not particularly trying to get your medical advice as much as philosophically, Guy. You can take a report from your doctor, stick it into ChachieBT, and more or less believe what Chachie... I mean, is that a wise thing or an unwise thing, what I just did?

27:00Dr. Lloyd Minor:I think the more information we get about our health, the better. Now, should we completely rely upon that? No. And that's why, as I said before, I don't think that supplants the need to have a conversation with, for example, whoever ordered the study, the MRI report that you just read, having a discussion with that provider, look, this is what I learned from chat GPT. Is this accurate? it. And let me give another example. I mentioned earlier in our conversation about this inner ear disorder that I described in 1998. For years, for many, many years after I described that, and I got referrals from around the country, around the world to do the surgery to correct the disorder when it was severe, most of the patients I saw for years were patients who had made their diagnosis from doing a simple Google search based upon their symptoms.

27:53Dr. Lloyd Minor:And the Google search pointed them to either our website or one of the myriad of other websites that described this disorder. And then they went to their doctor and said, I think I have this. And then they got referred. The point being that being able to get information about our health helps us to have the type of conversations we should be having with health care providers. So it's not saying that we do one or the other. It's saying that when we come informed with information, even if that information is not completely accurate, it may not be coming from a large language model that's still evolving.

28:32Dr. Lloyd Minor:But having that conversation driven by that information will help to get to the right answer, the right solution, more often than just coming in without having any background knowledge. So that would be my approach when I'm asked for advice. Use the large language models, but don't use them instead of, you know, seeking appropriate medical advice from people that you trust and providers that are really interested in your well-being. Okay.

28:58Guy Kawasaki:So now let's look into your crystal ball and let's say, Lloyd, in your wildest dreams, 10 years from now, I walk into the Stanford ER for treatment. I don't know, whatever, gunshot wound, I don't know, flu, COVID, whatever. I walk into the Stanford ER 10 years from now. What is your wildest fantasy how this will go that's different from today?

29:23Dr. Lloyd Minor:I would say it starts even before you get into the ER, that a wearable device that you have, you use, has already transmitted information pertaining to your pulse rate, your blood pressure, other information about your vital signs to the ER. They have that information before you even walk in the door. Your medical record has been assimilated, even though you got care at several different places where you've lived over the course of a decade. But all that information is assimilated and available before you come into the ER. You've had an opportunity to ping the ER to let them know in advance what your symptoms are.

30:10Dr. Lloyd Minor:So they're prepared when they see you to know, okay, you know, guy needs to be seen immediately by a physician because there's some concerning changes in his heart rate. Or this is a set of symptoms that maybe we should see guy in the urgent care area rather than the emergent care area in order to address the symptoms. So a lot of background work will have been done before you walk in the door. and then when you walk in the door, the treatment can already begin immediately. If there are signs that you need an EKG, then arrangements will have already been made for you to get that EKG. The room you go to in the ER to get the EKG done, all of that will be set up in advance.

30:55Dr. Lloyd Minor:Then the interpretation of that EKG is going to be informed by all the previous studies that you've had done in any health system because all that information will have been brought into your collated medical record. And then the physician who sees you will have that information, but more importantly, they will have a synopsis of what that information means in the context of the symptoms you're experiencing at the time. So that's how I hope and think encounters in the future will be driven by technology and by AI. It will then enable the providers who are taking care of you to really focus on your health and well-being, how your family's doing, much more than they've been able to in the past, because a lot of the steps I just described, they're having to do manually today.

31:43Guy Kawasaki:It seems to me that you are essentially saying that AI is not necessarily going to replace people. It's going to make healthcare better than what it is, right? Right Right now, you're reading these reports where UPS let 40 ,000 people go, IBM let 15 ,000, Salesforce 4 ,000, and everybody's saying it's because of AI. Well, I don't see AI driving the little brown trucks, but anyway. So, if people are afraid of AI eliminating jobs at Stanford Hospital, what do you say to that?

32:18Dr. Lloyd Minor:I don't think AI is going to eliminate jobs in healthcare. I think if we look at technology, the application of technology over human history, it has changed the nature of work in many jobs and in many professions. And that will likely occur in health care, too. Hopefully, it will make the work that health care providers do more meaningful. Hopefully, it will get more of us back to interfacing, interacting directly with patients and having the type of impact that drove us to be in health care for the first place. No, I don't think that AI is going to massively displace the need for healthcare providers.

32:57Dr. Lloyd Minor:I do think it's going to improve access to care and it's going to improve the efficiency and the effectiveness of care.

33:04Guy Kawasaki:So now, let's say I'm a hardcore skeptic and I'm listening to this and I'm saying, oh, these two Silicon Valley tech people are just like painting this wonderful picture. But what about all the horror stories I read about hallucinations and all that? But comfort me, Lloyd, like why can I believe in this future?

33:25Dr. Lloyd Minor:I think first the hallucinations are real. And that's why we should never be today relying solely on AI. You mentioned you giving your radiology imaging report to ChatGPD and getting a synopsis. I would never rely upon any synopsis prepared by any large language model today as being the absolute definitive truth. But it should drive a discussion between a patient and their provider. I think the other analogy, Guy, is when smartphones were first introduced, early versions of the iPhone, for example, there was a lot of skepticism that there'd be anything other than niche for a few people who could afford what was a fairly expensive device.

34:09Dr. Lloyd Minor:And, of course, that was in prehistoric times in terms of the smartphones we have today. Now, a huge proportion of people on the planet are using some form of a smartphone, and that's brought information and power to people who never had it before. And I think what devices like the iPhone enabled is going to be even supercharged further based upon what AI can bring to transforming information into knowledge. And it's going to be a really exciting decade ahead to see. It's not going to be without some disruptions and some problems along the way, for sure. But I think the future is really bright in terms of what this technology offers all of us.

34:55Dr. Lloyd Minor:To get back to what you referenced early on, and that is to really a focus on health and well-being. And for health and well-being being seen as much more than just the absence of disease.

35:06Guy Kawasaki:So I want to get really specific here. Talk to me about the role of agents in medicine. Like what's an agent AI going to do at Stanford?

35:19Dr. Lloyd Minor:Let me talk about where AI agents are being used today, early stages, but something that we're really excited about. We treat a lot of patients with cancer. And because we're an academic medical center, we're a referral center, we see a lot of patients with advanced cancer. So let's take the example of a person who comes in with non-smoker lung cancer. For reasons we don't understand yet, there's been a dramatic increase in non-smoker lung cancer in this country and around the world. Oftentimes, those cancers aren't identified until they are very advanced. So patients come in. Each patient's a little different.

36:00Dr. Lloyd Minor:The tumor pathology is a little different. The overall health of the patient is different. Now we discuss those cases in what's called a tumor board. Tumor board will have medical oncologists, will have lung surgeons, will have radiation oncologists, it will have people who are interested in endocrinology all coming together to discuss the findings and the diagnosis in that patient and collectively talk about what the best treatment approach is for the patient. Should the patient have surgery first and then targeted therapy? Should there be targeted therapies followed by surgery or no surgery at all?

36:39Dr. Lloyd Minor:Those discussions are based upon the medical literature. Today, what faculty in our Department of Biomedical Data Science working with people in the thoracic or lung oncology group are doing is using agents in each of the areas impacting that patient's diagnosis and treatment. For example, using agents that focus on the AI interpretation of the radiology images, using agents that are trained and focus on the pathology and an analysis of the slides taken from the tumor. And these agents then roll up in a collaborative way to a large language model that takes information furnished by the agents and coalesces those into a series of evidence-based recommendations that the experts at the tumor board can consider as they are discussing the case.

37:35Dr. Lloyd Minor:Now, the large language model is not telling the experts what to do, but it is giving insights that they may not have seen just from their own individual expertise being pooled in one room at one moment in time. So that's an example of each agent has been trained based upon, in this case, the images in that field, whether it's the MRI images or it's the pathology slide images. And then their findings roll up to a broader interpretation that considers the reports from a variety of different sources.

38:12Guy Kawasaki:And you're saying this is going to happen or it's happening right now?

38:17Dr. Lloyd Minor:This is happening right now in thoracic oncology in the early stages, but it's an area of focus right now for us. And we focused on these non-smoker lung cancers because the incidence has been growing and because it's a very challenging disease to treat. So we thought that the applications of AI could have perhaps the greatest impact in this area first, but it can be extended to any tumor type. But this is the area being initially focused on. Up next on Remarkable People. There is potentially a world where the mechanistic education that forms the backbone of medical education today may need to be less intense than it is today.

38:57Dr. Lloyd Minor:We are being very cautious about not backing away from what has been, I think, a tried and true mechanism for ensuring that physicians have the background knowledge needed not only to practice medicine today, but practice medicine in the future as knowledge changes. But there conceivably could be some significant changes in the future to how we train the next generation of doctors and how all of us in practice keep ourselves well-informed and well-trained in the future.

39:35Guy Kawasaki:We were awarded this incredible contract to put on an event for 2 ,000 people. The budget was around$1.5 million. It was a black tie event. There was a jazz band playing and a cigar bar and a bourbon bar. That's Natasha Miller, Capital One business customer and CEO of Entire Productions, a corporate event management company. And she's telling us how she had to float a large contract for nine months. It was really hard since we were carrying that$1.5 million, most of which passed through to pay for the venue and all the vendors and all the food and beverage.

40:11Dr. Lloyd Minor:It's this waterfall effect.

40:14Guy Kawasaki:Thankfully, we were able to handle that with the Capital One credit cards, which have a 2 % cash back on everything.

40:22Dr. Lloyd Minor:For Natasha, it wasn't just the financial support from Capital One Business, but the personal investment as well.

40:28Guy Kawasaki:I had incredible support from my personal banker, Callie. I knew that at any point I was in trouble that I can call her, which I've never had before in a bank, ever. With the help of Callie and her Capital One business card, Natasha was able to stretch every dollar in order to bring this monumental event to life. To learn more, go to CapitalOne.com slash business cards. Become a little more remarkable with each episode of Remarkable People. It's found on Apple Podcasts or wherever you listen to your favorite shows. Welcome back to Remarkable People with Guy Kawasaki. If I am a freshman in college and I didn't faint on my tour of the Stanford Hospital, so I decided to take organic chemistry and do the whole thing, Like, what's your advice to someone who wants to be a doctor in this future world where it's precision health and it's AI and agents and all that?

41:32Guy Kawasaki:How do you prepare for this new kind of medical career?

41:36Dr. Lloyd Minor:Passion determines a lot in life. And what I tell students, and I do teach a section of our course on citizenship that's offered in the fall quarter to all Stanford freshmen. When people ask me for career advice, they say, use college to define your passion. Explore different fields, but choose an area of focus that you're really, really excited about because your excitement will drive your engagement, will enable you to work hard, to overcome adversity. that's frequently determined more by the passion we have or as much by the passion we have for a discipline as it is by whatever innate ability is or other factors.

42:23Dr. Lloyd Minor:So define your passion and then go after it and pursue all the resources you can to make sure that you're succeeding. That's the advice I give to students at all levels when they ask me about careers. It certainly was the case in my career when I took a course as an undergraduate that looked at models, mathematical models of how the inner ear balance system works as being an example of how you can use what's called linear systems analysis to analyze systems in the body. And I just thought, this is really cool stuff, you know, and I want to have impact in that area. I want to do that science. And that guided me through my scientific and my clinical career.

43:05Dr. Lloyd Minor:And then along the way, picked up a desire to have an impact as a leader as well. But in the end of the day, go with what you're passionate about and then pursue it with vigor and with valor. And you'll be happy and you'll have impact by doing that.

43:21Guy Kawasaki:Are there already courses about the application of AI that are taught to medical students?

43:29Dr. Lloyd Minor:A lot. In fact, just this year, an associate dean, Stanford MD, PhD, who is looking at how we roll out AI in our curriculum. We're certainly teaching our students about the basics of how foundation models work because we are a scientifically focused medical school. We want to train physician scientists. We have a cohort of students who have deep disciplinary expertise in AI and go on to get advanced degrees in fields related to AI, whether that be in our Department of Biomedical Data Science or even our Department of Computer Science in the School of Engineering. And it's important to have a cohort of physicians who are, if you will, bilingual.

44:08Dr. Lloyd Minor:In other words, they really understand the deep components of foundation models, and they know what it is to be a practicing physician. But for all physicians and all physicians in training, we should have an understanding of what the basis for these large language models is, how they are trained, how they can be helpful, but also what their limitations can be and how biases based upon the way they're trained can seep into the interpretations that they're giving us.

44:38Guy Kawasaki:Do you think that AI's impact in medicine is going to reduce the necessity for memorization? When we're talking about drug interactions, you talked about how much you have to memorize, right? So now, is that necessary anymore? Is that a wise use of school time and stuff because of the amount of information at your fingertips at this point?

45:02Dr. Lloyd Minor:Yes, it already has, Guy, reduced the amount of memorization. No one memorizes the doses of drugs anymore. And there are other aspects where memorization is being de-emphasized. I think what remains to be determined, there's still a lot of scientific medical education that's focused on understanding mechanisms. Today, physician education, the closest analogy that I know of, it's not an exact analogy, but it's like learning a foreign language. What do you have to do to learn a foreign language? You have to know the vocabulary. You have to know the grammar, the way the words are connected in sentences and in structures.

45:41Dr. Lloyd Minor:And then you have to be able to use the vocabulary and the grammar to communicate. Well, that's a little bit like what goes on in medical education. You have to know the vocabulary. You need to know the muscles, the bones. You need to know the biochemical mechanisms. You need to know the grammar, so how cells relate to each other, how cells interact to form organs. And there's a scientific basis for all of that. What remains to be determined is with large language models that are statistically inferential in nature, how much mechanistic understanding do you need to have to be not only an excellent physician, but also to be a researcher in the future?

46:22Dr. Lloyd Minor:Because if models are sufficiently well-trained and their approaches today, companies focused on enhancing drug discovery with AI, where models are being trained with every piece of information that can be found about the chemical interactions in a cell, about the genetic makeup of a cell, how that genetic makeup changes over time. And then armed with that information, the large language models are able to statistically deduce what would happen if you did this, that, or the other. So there is potentially a world where the mechanistic education that forms the backbone of medical education today may need to be less intense than it is today.

47:03Dr. Lloyd Minor:We are being very cautious about not backing away from what has been, I think, a tried and true mechanism for ensuring that physicians have the background knowledge needed not only to practice medicine today, but practice medicine in the future as knowledge changes. But there conceivably could be some significant changes in the future to how we train the next generation of doctors and how all of us in practice keep ourselves well informed and well trained in the future.

47:34Guy Kawasaki:So are you saying that house is out of business? There's not going to be it. I sure hope not. So if I'm listening to this and I'm thinking, oh, this is all great. It's going to be really promising, but Lloyd, Dr. Miner, what do I do if I want to do research about some symptom I'm having or something like, do I just go to LLM? Do I go to Google? Do I go to Gemini? Or do I go to the Mayo Clinic site? What would you trust for medical information today? And including the CDC and the FDA. What do I trust anymore?

48:17Dr. Lloyd Minor:Exactly. I think it depends a little bit on the condition. And for individual conditions, there are going to be sites, approaches that are perhaps more informative than others. But one very important point, we touched on it before, get as much information as you can for sure. And there's so much information available out there, much of it, most of it at no charge at all. Get as much information as you can and then use that information to have the conversation that you should be having with your physician, your health care provider. I think at this stage, it's not a good idea just to rely upon large language models, any source of information without consulting a human who's informed, who has your well-being at the heart and core of their interaction with you to have a conversation about it.

49:09Dr. Lloyd Minor:It's what I mentioned before with superior canality Hissen syndrome and patients coming to their doctor saying, I think I may have this. You know, many of them didn't, and that's okay, but they still had the conversation. And those that did were able to get the treatment that they needed, which they might not have gotten because it takes a while for medical information to catch up with the way people are practicing. Use AI as an enabler, but don't use it instead of your interaction with healthcare providers.

49:40Guy Kawasaki:Let's say that, take it as a given that Stanford and you are probably the leading edge of this kind of implementation, but if I'm a random doctor, I'm working for Kaiser or Palo Alto Medical Clinic or Sutter Health or something, and I'm listening to this, I say, so, you know, Dr. Lloyd. Tell me as a physician out there in the field, how can I prepare for this world? How can I optimize my knowledge and my practice to take advantage of this to provide the best healthcare possible to my patients? What do I do right now?

50:16Dr. Lloyd Minor:I think explore all of the models. And they're also now curated large language models that have been trained on the medical literature that are available. Explore them, learn from them, and if you will, play around with them. That's the way you're going to become at ease, both at what works and what doesn't work, but never interpret what you see as being the final answer or as being the absolute truth until you are able to reconcile it and triangulate it with multiple sources of information as well as with your own expertise. But use them as an extraordinarily valuable learning tool because they are.

51:03Guy Kawasaki:How about naming some names when you just said there are large language models that are specifically trained on medical information? Can you name some names?

51:14Dr. Lloyd Minor:Well, one that many people are using is a model company approach called Open Evidence that has been trained and curated based upon the published medical literature, as well as human sort of intervention in terms of reading responses to make sure that they're cogent, as well as the fact that it gives references to the primary source literature when it's giving a response to your query. That's helpful because you can simply click on or go to the reference and see, did this large language model really interpret the study correctly? That's one. There are others out there that are using similar approaches.

51:55Dr. Lloyd Minor:That just happens to be the one that I'm most familiar with, but I'm not recommending open evidence over other things available. There are also approaches today and models today that will allow you to take a PDF of a scientific paper, give it to the large language model and say, give me a synopsis of this paper. Or even better, you could take a half dozen papers that on a particular topic that maybe have disparate conclusions and say, help me understand the differences in the interpretation of the data between these studies. Now there you have to be careful because sometimes it's valuable and sometimes it's not.

52:37Dr. Lloyd Minor:But these are tools that are out there available for general use. And we should be taking advantage of them. First of all, the more we use them, the more we give feedback on them, the better the models are going to be. And just having the experience of interacting with the model and really delving in deeply to what it's telling you and being skeptical, that helps us all to be better learners. So it's a win-win all around.

53:04Guy Kawasaki:I think that is the place to end this recording. This has been very interesting. The irony of me dropping out of med school and now talking to the person who runs the whole thing. There's some justice there. I thank you very much, Lloyd Minor. Thank you, Guy, for all that you do. It's been a pleasure. It's been a pleasure. And I get up to the Bay Area all the time. Sometime maybe I'll stop over. Please do. I'd love to get together with you. Yeah, we'll go have lunch in your cafeteria. It's great. We have healthy food. Yeah, thank you very much. I want to thank Madison Neismar, co-producer, Jeff C., who has been listening all this time, and Shannon Hernandez, sound design engineer, and Tessa Neismar, our researcher.

53:51Guy Kawasaki:So that's the team behind me. And I thank you very much. And I hope I only ever see you socially. How's that?

54:04Guy Kawasaki:This is Remarkable People.