How AI Is Changing Healthcare for Patients & Doctors | Dr. Fei-Fei Li & Dr. Andrew Huberman

AI in Healthcare & Biology

00:00Since uh you're here, I'm going to go next to something that I think most everybody would agree would be a wonderful thing if it existed and it's already starting to happen, which is the use of AI to augment health discovery, treatment of disease and so on.

00:13So using the AlphaGo example from before and people surely still remember the cat example, those just follow certain rules. Alph Go is very complicated set of rules, but if you learn them, there's a constrained set of rules. With the cat, it seems unconstrained, like infinite possibilities, but it's constrained enough that machines and humans can learn it really well.

00:37When you start getting into medicine, there are rules of medicine. There are rules of science. You have a question, you pose a hypothesis, you test the hypothesis, you try and rule out your hypo and so on like the the scientific method. And in medicine, every field has its methods.

00:52We observe, we observe disease, we observe who recovers, we have a case report, we do a randomized control trial. So there are rules and the internet knows these rules. So LLMs can be used to mine health information very well because there are constrained rules.

01:06But I think you and I both know because I also consider you a biologist that the rules of biology are still revealing themselves to us. Which is not to say that the dermatologists, neurosurgeons, and oncologists don't know what they're doing, but they're doing what they're doing within a constrained set of rules that they learned.

01:23And even if they continue to learn and update them, it's every month it seems now that a discovery comes out that violates the rule. Like I learned that action potentials are unitary. They always look the same. You either fire or not. But there was a paper not but 12 years ago that showed that the shape of an action potential can vary quite a lot.

01:42It was published in Nature. Mhm. Everyone saw it and then no one wanted to deal with it. It's just too much. It changes the rule. Neurons are supposed to be either graded or all are one. And the all I mean it's in every single textbook. So now if I take a bunch of neural activity and I give it the rule, oh well you know action potentials can be big, they can be small in the same neuron.

02:01It completely confuses everything we understand about neuroscience and it just our understanding of the brain just breaks down to zero. Yeah. But if you gave AI the rule that it could be, you know, a hundred different shapes of this signal, well, AI could probably do a lot more than even the very very best graduate student at dare I say Stanford or to be fair MIT or Caltech.

02:24I don't think it can do it and it can do it like in the duration of this question, which admittedly is a bit long. So, I'd like to get your thoughts on how is it that humans in health care, the general public and AI can collaborate to help solve disease and ideally come up with new rules for discovery so that we can finally understand our biology at a level that can really change the course of humanity

AI for Scientific Discovery

02:50for the better. Yeah. No, Andrew, this is probably perhaps you touch one of the most exciting usage of AI, which is scientific discovery. And in the case of biio medicine, you know, scientific discovery directly connects to human health and diseases, I think we're we're ready for complete re rewriting of how scientific discovery can be done because for ages, I don't even know how long, it relies on smart humans retaining what they have learned from other smart humans and and and doing things at the speed of our own muscles, I guess, you know.

03:28Most likely of course there's like super colliders and and all that but by and large the the ways of doing scientific discovery human brain or scientists brain are the only central character in this process. Now we have a new tool whose brain that can retain humongous amount of information can help us synthesize knowledge can go across disciplines in ways that you and I cannot go.

04:01So for example we happen to be both in the vision neuroscience AI domain. I know nothing about you know oactory zero like I don't even know how to spell most of probably the these words in that our colleagues know right so it's so hard for our brain but now we have a tool that can break open so so I think that we need to change we need to use this tool we absolutely I I was just thinking 150 or I don't know exactly when years ago we electricity changed everything in in in our life, right?

04:44I'm sure that's a moment we were thinking about how the changes, the opportunities, the scary moment. I think we have to come to

Using AI for Personal Diagnosis

04:55reckon that scientific discovery is one of the most exciting opportunity for AI and for health, right? How information can be synthesized, how information can be presented not only to clinicians but also to patients and how patients can participate in that process from diagnosis to treatment is also there's just so much we can do now.

05:24Yeah. I mean AI I won't say AI is better than all doctors but AI was able to disambiguate vertigo from low blood pressure for me a few months back and one of the people who got it wrong is a ENT who works on the vestibular system. What information did you provide just your subjective?

05:44My subjective experience over a day or two. Okay. Um, turns out it was a medication that a doctor had prescribed me that I had a like a mild but adverse event. And it's a weird thing to step and feel like the whole world's dropping down and then kind of spinning and I thought, "Oh my goodness, this is like feels like vertigo."

06:00But I remember dizzy and lightheaded or different. So I started like looking into that and then and um sure enough it was a it was a blood pressure issue. It brought brought my blood pressure, excuse me, down too low. And but I consulted we know some smart doctors.

06:15Um none of these were at Stanford. I will say that this is the truth. But it was we should just be intellectually honest. But it's just remarkable. And when I ran it back to them, they were like, "That's really incredible." You know, had you not been on the phone with me and in my clinic, I would have been able to do some additional testing to be fair.

06:28But this was zero cost. It took a morning to know if I drank some uh electrolytes at what I would have thought would be excessive level that by two hours later, I would be fine. Now, of course, there's the possibility of a placebo effect here, but two hours later, I was fine.

06:48And so, it's also very consoling to the patient to have this. And so, it's not to say don't go to a doctor, but it it's

Robotic Surgery & AI Collaboration

06:55incredible. I mean, this exists now. The doctor can use this tooling. By the way, I have a very interesting example. You know that we have to reschedu this uh our conversation because my father was going through a surgery right at Stanford uh with an incredible surgeon.

07:11But the surgery was done by a robot, the Davinci robot system because it was a liver surgery and the surgeon, incredible surgeon was driving the robot. So it was a deep human machine collaboration. After the surgery, I asked the surgeon, I said, "Do you imagine if say you've done a million, which is impossible for a surgeon, but human surgeon, but let's collect all of human surgeons uh for for this liver, this type of liver surgery data.

07:41Can we possibly train a automatic AI to do this?" The answer was not clear. So we went a little bit down the rabbit hole because liver is a very complicated organ. It's extremely vascular. It has a lot of vessels and everybody's liver is very different.

08:04So given the reality of how many patients undergo liver surgery per year, even if you aggregate um the world's liver patient um surgeries, you might not have enough data to train these algorithm. So this speaks of a very important fact that um AI learns from patterns.

08:27When the patterns are not abundant, then we have to be careful. We have to know how to use AI or how not to use AI. You know in this case that having a human collaborating with the robot is way better than a underlearned robot doing the surgery by itself.

08:45But the same issue might be true for surgeons because how many surgeries a surgeon can get trained on. So these are opportunities that humans and AI can totally collaborate with and might reveal the best result. Right now the future remains to be seen.

09:03Can we create a artificial simulation of a liver that we can now train infinite possibility? These are all incredibly open scientific possibilities that is waiting ahead of us. But then there are uh situations like your situation where the vertigo versus low blood pressure probably have been reported so many times that in the database there's enough of that that AI has learned that so we can then now take advantage of that for people who don't have immediate access to doctors.

09:43Amazing. Is your father's surgery went okay? It did. It actually lost 10x less blood than a typical surgery uh thanks to the laparoscopic capability of a robot.