Transcript [MUSIC]      [BOOK PASSAGE]   PETER LEE: “In ‘The Little Black Bag,’ a classic science fiction story, a high-tech doctor’s kit of the future is accidentally transported back to the 1950s, into the shaky hands of a washed-up, alcoholic doctor. The ultimate medical tool, it redeems the doctor wielding it, allowing him to practice gratifyingly heroic medicine. … The tale ends badly for the doctor and his treacherous assistant, but it offered a picture of how advanced technology could transform medicine—powerful when it was written nearly 75 years ago and still so today. What would be the Al equivalent of that little black bag? At this moment when new capabilities are emerging, how do we imagine them into medicine?”   [END OF BOOK PASSAGE]     [THEME MUSIC]     This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?     In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.   [THEME MUSIC FADES] The book passage I read at the top is from “Chapter 10: The Big Black Bag.”  In imagining AI in medicine, Carey, Zak, and I included in our book two fictional accounts. In the first, a medical resident consults GPT-4 on her personal phone as the patient in front of her crashes. Within seconds, it offers an alternate response based on recent literature. In the second account, a 90-year-old woman with several chronic conditions is living independently and receiving near-constant medical support from an AI aide.    In our conversations with the guests we’ve spoken to so far, we’ve caught a glimpse of these predicted futures, seeing how clinicians and patients are actually using AI today and how developers are leveraging the technology in the healthcare products and services they’re creating. In fact, that first fictional account isn’t so fictional after all, as most of the doctors in the real world actually appear to be using AI at least occasionally—and sometimes much more than occasionally—to help in their daily clinical work. And as for the second fictional account, which is more of a science fiction account, it seems we are indeed on the verge of a new way of delivering and receiving healthcare, though the future is still very much open.  As we continue to examine the current state of AI in healthcare and its potential to transform the field, I’m pleased to welcome Bill Gates and Sébastien Bubeck.   Bill may be best known as the co-founder of Microsoft, having created the company with his childhood friend Paul Allen in 1975. He’s now the founder of Breakthrough Energy, which aims to advance clean energy innovation, and TerraPower, a company developing groundbreaking nuclear energy and science technologies. He also chairs the world’s largest philanthropic organization, the Gates Foundation, and focuses on solving a variety of health challenges around the globe and here at home.  Sébastien is a research lead at OpenAI. He was previously a distinguished scientist, vice president of AI, and a colleague of mine here at Microsoft, where his work included spearheading the development of the family of small language models known as Phi. While at Microsoft, he also coauthored the discussion-provoking 2023 paper “Sparks of Artificial General Intelligence,” which presented the results of early experiments with GPT-4 conducted by a small team from Microsoft Research.    [TRANSITION MUSIC]   Here’s my conversation with Bill Gates and Sébastien Bubeck.  LEE: Bill, welcome.  BILL GATES: Thank you.  LEE: Seb …  SÉBASTIEN BUBECK: Yeah. Hi, hi, Peter. Nice to be here.  LEE: You know, one of the things that I’ve been doing just to get the conversation warmed up is to talk about origin stories, and what I mean about origin stories is, you know, what was the first contact that you had with large language models or the concept of generative AI that convinced you or made you think that something really important was happening?  And so, Bill, I think I’ve heard the story about, you know, the time when the OpenAI folks—Sam Altman, Greg Brockman, and others—showed you something, but could we hear from you what those early encounters were like and what was going through your mind?   GATES: Well, I’d been visiting OpenAI soon after it was created to see things like GPT-2 and to see the little arm they had that was trying to match human manipulation and, you know, looking at their games like Dota that they were trying to get as good as human play. And honestly, I didn’t think the language model stuff they were doing, even when they got to GPT-3, would show the ability to learn, you know, in the same sense that a human reads a biology book and is able to take that knowledge and access it not only to pass a test but also to create new medicines.  And so my challenge to them was that if their LLM could get a five on the advanced placement biology test, then I would say, OK, it took biologic knowledge and encoded it in an accessible way and that I didn’t expect them to do that very quickly but it would be profound.   And it was only about six months after I challenged them to do that, that an early version of GPT-4 they brought up to a dinner at my house, and in fact, it answered most of the questions that night very well. The one it got totally wrong, we were … because it was so good, we kept thinking, Oh, we must be wrong. It turned out it was a math weakness [LAUGHTER] that, you know, we later understood that that was an area of, weirdly, of incredible weakness of those early models. But, you know, that was when I realized, OK, the age of cheap intelligence was at its beginning.  LEE: Yeah. So I guess it seems like you had something similar to me in that my first encounters, I actually harbored some skepticism. Is it fair to say you were skeptical before that?  GATES: Well, the idea that we’ve figured out how to encode and access knowledge in this very deep sense without even understanding the nature of the encoding, …  LEE: Right.   GATES: … that is a bit weird.   LEE: Yeah.  GATES: We have an algorithm that creates the computation, but even say, OK, where is the president’s birthday stored in there? Where is this fact stored in there? The fact that even now when we’re playing around, getting a little bit more sense of it, it’s opaque to us what the semantic encoding is, it’s, kind of, amazing to me. I thought the invention of knowledge storage would be an explicit way of encoding knowledge, not an implicit statistical training.  LEE: Yeah, yeah. All right. So, Seb, you know, on this same topic, you know, I got—as we say at Microsoft—I got pulled into the tent. [LAUGHS]  BUBECK: Yes.   LEE: Because this was a very secret project. And then, um, I had the opportunity to select a small number of researchers in MSR [Microsoft Research] to join and start investigating this thing seriously. And the first person I pulled in was you.  BUBECK: Yeah.  LEE: And so what were your first encounters? Because I actually don’t remember what happened then.  BUBECK: Oh, I remember it very well. [LAUGHS] My first encounter with GPT-4 was in a meeting with the two of you, actually. But my kind of first contact, the first moment where I realized that something was happening with generative AI, was before that. And I agree with Bill that I also wasn’t too impressed by GPT-3.  I though that it was kind of, you know, very naturally mimicking the web, sort of parroting what was written there in a nice way. Still in a way which seemed very impressive. But it wasn’t really intelligent in any way. But shortly after GPT-3, there was a model before GPT-4 that really shocked me, and this was the first image generation model, DALL-E 1.  So that was in 2021. And I will forever remember the press release of OpenAI where they had this prompt of an avocado chair and then you had this image of the avocado chair. [LAUGHTER] And what really shocked me is that clearly the model kind of “understood” what is a chair, what is an avocado, and was able to merge those concepts.  So this was really, to me, the first moment where I saw some understanding in those models.   LEE: So this was, just to get the timing right, that was before I pulled you into the tent.  BUBECK: That was before. That was like a year before.  LEE: Right.   BUBECK: And now I will tell you how, you know, we went from that moment to the meeting with the two of you and GPT-4.  So once I saw this kind of understanding, I thought, OK, fine. It understands concept, but it’s still not able to reason. It cannot—as, you know, Bill was saying—it cannot learn from your document. It cannot reason.   So I set out to try to prove that. You know, this is what I was in the business of at the time, trying to prove things in mathematics. So I was trying to prove that basically autoregressive transformers could never reason. So I was trying to prove this. And after a year of work, I had something reasonable to show. And so I had the meeting with the two of you, and I had this example where I wanted to say, there is no way that an LLM is going to be able to do x.  And then as soon as I … I don’t know if you remember, Bill. But as soon as I said that, you said, oh, but wait a second. I had, you know, the OpenAI crew at my house recently, and they showed me a new model. Why don’t we ask this new model this question?   LEE: Yeah. BUBECK: And we did, and it solved it on the spot. And that really, honestly, just changed my life. Like, you know, I had been working for a year trying to say that this was impossible. And just right there, it was shown to be possible.   LEE: [LAUGHS] One of the very first things I got interested in—because I was really thinking a lot about healthcare—was healthcare and medicine.  And I don’t know if the two of you remember, but I ended up doing a lot of tests. I ran through, you know, step one and step two of the US Medical Licensing Exam. Did a whole bunch of other things. I wrote this big report. It was, you know, I can’t remember … a couple hundred pages.   And I needed to share this with someone. I didn’t … there weren’t too many people I could share it with. So I sent, I think, a copy to you, Bill. Sent a copy to you, Seb.   I hardly slept for about a week putting that report together. And, yeah, and I kept working on it. But I was far from alone. I think everyone who was in the tent, so to speak, in those early days was going through something pretty similar. All right. So I think … of course, a lot of what I put in the report also ended up being examples that made it into the book.  But the main purpose of this conversation isn’t to reminisce about [LAUGHS] or indulge in those reminiscences but to talk about what’s happening in healthcare and medicine. And, you know, as I said, we wrote this book. We did it very, very quickly. Seb, you helped. Bill, you know, you provided a review and some endorsements.  But, you know, honestly, we didn’t know what we were talking about because no one had access to this thing. And so we just made a bunch of guesses. So really, the whole thing I wanted to probe with the two of you is, now with two years of experience out in the world, what, you know, what do we think is happening today?  You know, is AI actually having an impact, positive or negative, on healthcare and medicine? And what do we now think is going to happen in the next two years, five years, or 10 years? And so I realize it’s a little bit too abstract to just ask it that way. So let me just try to narrow the discussion and guide us a little bit.   Um, the kind of administrative and clerical work, paperwork, around healthcare—and we made a lot of guesses about that—that appears to be going well, but, you know, Bill, I know we’ve discussed that sometimes that you think there ought to be a lot more going on. Do you have a viewpoint on how AI is actually finding its way into reducing paperwork?  GATES: Well, I’m stunned … I don’t think there should be a patient-doctor meeting where the AI is not sitting in and both transcribing, offering to help with the paperwork, and even making suggestions, although the doctor will be the one, you know, who makes the final decision about the diagnosis and whatever prescription gets done.   It’s so helpful. You know, when that patient goes home and their, you know, son who wants to understand what happened has some questions, that AI should be available to continue that conversation. And the way you can improve that experience and streamline things and, you know, involve the people who advise you. I don’t understand why that’s not more adopted, because there you still have the human in the loop making that final decision.  But even for, like, follow-up calls to make sure the patient did things, to understand if they have concerns and knowing when to escalate back to the doctor, the benefit is incredible. And, you know, that thing is ready for prime time. That paradigm is ready for prime time, in my view.  LEE: Yeah, there are some good products, but it seems like the number one use right now—and we kind of got this from some of the previous guests in previous episodes—is the use of AI just to respond to emails from patients. [LAUGHTER] Does that make sense to you?  BUBECK: Yeah. So maybe I want to second what Bill was saying but maybe take a step back first. You know, two years ago, like, the concept of clinical scribes, which is one of the things that we’re talking about right now, it would have sounded, in fact, it sounded two years ago, borderline dangerous. Because everybody was worried about hallucinations. What happened if you have this AI listening in and then it transcribes, you know, something wrong?  Now, two years later, I think it’s mostly working. And in fact, it is not yet, you know, fully adopted. You’re right. But it is in production. It is used, you know, in many, many places. So this rate of progress is astounding because it wasn’t obvious that we would be able to overcome those obstacles of hallucination. It’s not to say that hallucinations are fully solved. In the case of the closed system, they are.   Now, I think more generally what’s going on in the background is that there is something that we, that certainly I, underestimated, which is this management overhead. So I think the reason why this is not adopted everywhere is really a training and teaching aspect. People need to be taught, like, those systems, how to interact with them.  And one example that I really like, a study that recently appeared where they tried to use ChatGPT for diagnosis and they were comparing doctors without and with ChatGPT (opens in new tab). And the amazing thing … so this was a set of cases where the accuracy of the doctors alone was around 75%. ChatGPT alone was 90%. So that’s already kind of mind blowing. But then the kicker is that doctors with ChatGPT was 80%.   Intelligence alone is not enough. It’s also how it’s presented, how you interact with it. And ChatGPT, it’s an amazing tool. Obviously, I absolutely love it. But it’s not … you don’t want a doctor to have to type in, you know, prompts and use it that way.  It should be, as Bill was saying, kind of running continuously in the background, sending you notifications. And you have to be really careful of the rate at which those notifications are being sent. Because if they are too frequent, then the doctor will learn to ignore them. So you have to … all of those things matter, in fact, at least as much as the level of intelligence of the machine.  LEE: One of the things I think about, Bill, in that scenario that you described, doctors do some thinking about the patient when they write the note. So, you know, I’m always a little uncertain whether it’s actually … you know, you wouldn’t necessarily want to fully automate this, I don’t think. Or at least there needs to be some prompt to the doctor to make sure that the doctor puts some thought into what happened in the encounter with the patient. Does that make sense to you at all?  GATES: At this stage, you know, I’d still put the onus on the doctor to write the conclusions and the summary and not delegate that.  The tradeoffs you make a little bit are somewhat dependent on the situation you’re in. If you’re in Africa, So, yes, the doctor’s still going to have to do a lot of work, but just the quality of letting the patient and the people around them interact and ask questions and have things explained, that alone is such a quality improvement. It’s mind blowing.   LEE: So since you mentioned, you know, Africa—and, of course, this touches on the mission and some of the priorities of the Gates Foundation and this idea of democratization of access to expert medical care—what’s the most interesting stuff going on right now? Are there people and organizations or technologies that are impressing you or that you’re tracking?  GATES: Yeah. So the Gates Foundation has given out a lot of grants to people in Africa doing education, agriculture but more healthcare examples than anything. And the way these things start off, they often start out either being patient-centric in a narrow situation, like, OK, I’m a pregnant woman; talk to me. Or, I have infectious disease symptoms; talk to me. Or they’re connected to a health worker where they’re helping that worker get their job done. And we have lots of pilots out, you know, in both of those cases.   The dream would be eventually to have the thing the patient consults be so broad that it’s like having a doctor available who understands the local things.   LEE: Right.   GATES: We’re not there yet. But over the next two or three years, you know, particularly given the worsening financial constraints against African health systems, where the withdrawal of money has been dramatic, you know, figuring out how to take this—what I sometimes call “free intelligence”—and build a quality health system around that, we will have to be more radical in low-income countries than any rich country is ever going to be.   LEE: Also, there’s maybe a different regulatory environment, so some of those things maybe are easier? Because right now, I think the world hasn’t figured out how to and whether to regulate, let’s say, an AI that might give a medical diagnosis or write a prescription for a medication.  BUBECK: Yeah. I think one issue with this, and it’s also slowing down the deployment of AI in healthcare more generally, is a lack of proper benchmark. Because, you know, you were mentioning the USMLE [United States Medical Licensing Examination], for example. That’s a great test to test human beings and their knowledge of healthcare and medicine. But it’s not a great test to give to an AI.  It’s not asking the right questions. So finding what are the right questions to test whether an AI system is ready to give diagnosis in a constrained setting, that’s a very, very important direction, which to my surprise, is not yet accelerating at the rate that I was hoping for.  LEE: OK, so that gives me an excuse to get more now into the core AI tech because something I’ve discussed with both of you is this issue of what are the right tests. And you both know the very first test I give to any new spin of an LLM is I present a patient, the results—a mythical patient—the results of my physical exam, my mythical physical exam. Maybe some results of some initial labs. And then I present or propose a differential diagnosis. And if you’re not in medicine, a differential diagnosis you can just think of as a prioritized list of the possible diagnoses that fit with all that data. And in that proposed differential, I always intentionally make two mistakes.  I make a textbook technical error in one of the possible elements of the differential diagnosis, and I have an error of omission. And, you know, I just want to know, does the LLM understand what I’m talking about? And all the good ones out there do now. But then I want to know, can it spot the errors? And then most importantly, is it willing to tell me I’m wrong, that I’ve made a mistake?   That last piece seems really hard for AI today. And so let me ask you first, Seb, because at the time of this taping, of course, there was a new spin of GPT-4o last week that became overly sycophantic. In other words, it was actually prone in that test of mine not only to not tell me I’m wrong, but it actually praised me for the creativity of my differential. [LAUGHTER] What’s up with that?  BUBECK: Yeah, I guess it’s a testament to the fact that training those models is still more of an art than a science. So it’s a difficult job. Just to be clear with the audience, we have rolled back that [LAUGHS] version of GPT-4o, so now we don’t have the sycophant version out there.  Yeah, no, it’s a really difficult question. It has to do … as you said, it’s very technical. It has to do with the post-training and how, like, where do you nudge the model? So, you know, there is this very classical by now technique called RLHF [reinforcement learning from human feedback], where you push the model in the direction of a certain reward model. So the reward model is just telling the model, you know, what behavior is good, what behavior is bad.  But this reward model is itself an LLM, and, you know, Bill was saying at the very beginning of the conversation that we don’t really understand how those LLMs deal with concepts like, you know, where is the capital of France located? Things like that. It is the same thing for this reward model. We don’t know why it says that it prefers one output to another, and whether this is correlated with some sycophancy is, you know, something that we discovered basically just now. That if you push too hard in optimization on this reward model, you will get a sycophant model.  So it’s kind of … what I’m trying to say is we became too good at what we were doing, and we ended up, in fact, in a trap of the reward model.  LEE: I mean, you do want … it’s a difficult balance because you do want models to follow your desires and …  BUBECK: It’s a very difficult, very difficult balance.  LEE: So this brings up then the following question for me, which is the extent to which we think we’ll need to have specially trained models for things. So let me start with you, Bill. Do you have a point of view on whether we will need to, you know, quote-unquote take AI models to med school? Have them specially trained? Like, if you were going to deploy something to give medical care in underserved parts of the world, do we need to do something special to create those models?  GATES: We certainly need to teach them the African languages and the unique dialects so that the multimedia interactions are very high quality. We certainly need to teach them the disease prevalence and unique disease patterns like, you know, neglected tropical diseases and malaria. So we need to gather a set of facts that somebody trying to go for a US customer base, you know, wouldn’t necessarily have that in there.  Those two things are actually very straightforward because the additional training time is small. I’d say for the next few years, we’ll also need to do reinforcement learning about the context of being a doctor and how important certain behaviors are. Humans learn over the course of their life to some degree that, I’m in a different context and the way I behave in terms of being willing to criticize or be nice, you know, how important is it? Who’s here? What’s my relationship to them?   Right now, these machines don’t have that broad social experience. And so if you know it’s going to be used for health things, a lot of reinforcement learning of the very best humans in that context would still be valuable. Eventually, the models will, having read all the literature of the world about good doctors, bad doctors, it’ll understand as soon as you say, “I want you to be a doctor diagnosing somebody.” All of the implicit reinforcement that fits that situation, you know, will be there. LEE: Yeah. GATES: And so I hope three years from now, we don’t have to do that reinforcement learning. But today, for any medical context, you would want a lot of data to reinforce tone, willingness to say things when, you know, there might be something significant at stake.  LEE: Yeah. So, you know, something Bill said, kind of, reminds me of another thing that I think we missed, which is, the context also … and the specialization also pertains to different, I guess, what we still call “modes,” although I don’t know if the idea of multimodal is the same as it was two years ago. But, you know, what do you make of all of the hubbub around—in fact, within Microsoft Research, this is a big deal, but I think we’re far from alone—you know, medical images and vision, video, proteins and molecules, cell, you know, cellular data and so on.  BUBECK: Yeah. OK. So there is a lot to say to everything … to the last, you know, couple of minutes. Maybe on the specialization aspect, you know, I think there is, hiding behind this, a really fundamental scientific question of whether eventually we have a singular AGI [artificial general intelligence] that kind of knows everything and you can just put, you know, explain your own context and it will just get it and understand everything.  That’s one vision. I have to say, I don’t particularly believe in this vision. In fact, we humans are not like that at all. I think, hopefully, we are general intelligences, yet we have to specialize a lot. And, you know, I did myself a lot of RL, reinforcement learning, on mathematics. Like, that’s what I did, you know, spent a lot of time doing that. And I didn’t improve on other aspects. You know, in fact, I probably degraded in other aspects. [LAUGHTER] So it’s … I think it’s an important example to have in mind.  LEE: I think I might disagree with you on that, though, because, like, doesn’t a model have to see both good science and bad science in order to be able to gain the ability to discern between the two?  BUBECK: Yeah, no, that absolutely. I think there is value in seeing the generality, in having a very broad base. But then you, kind of, specialize on verticals. And this is where also, you know, open-weights model, which we haven’t talked about yet, are really important because they allow you to provide this broad base to everyone. And then you can specialize on top of it.  LEE: So we have about three hours of stuff to talk about, but our time is actually running low. BUBECK: Yes, yes, yes.   LEE: So I think I want … there’s a more provocative question. It’s almost a silly question, but I need to ask it of the two of you, which is, is there a future, you know, where AI replaces doctors or replaces, you know, medical specialties that we have today? So what does the world look like, say, five years from now?  GATES: Well, it’s important to distinguish healthcare discovery activity from healthcare delivery activity. We focused mostly on delivery. I think it’s very much within the realm of possibility that the AI is not only accelerating healthcare discovery but substituting for a lot of the roles of, you know, I’m an organic chemist, or I run various types of assays. I can see those, which are, you know, testable-output-type jobs but with still very high value, I can see, you know, some replacement in those areas before the doctor.   The doctor, still understanding the human condition and long-term dialogues, you know, they’ve had a lifetime of reinforcement of that, particularly when you get into areas like mental health. So I wouldn’t say in five years, either people will choose to adopt it, but it will be profound that there’ll be this nearly free intelligence that can do follow-up, that can help you, you know, make sure you went through different possibilities.  And so I’d say, yes, we’ll have doctors, but I’d say healthcare will be massively transformed in its quality and in efficiency by AI in that time period.  LEE: Is there a comparison, useful comparison, say, between doctors and, say, programmers, computer programmers, or doctors and, I don’t know, lawyers?  GATES: Programming is another one that has, kind of, a mathematical correctness to it, you know, and so the objective function that you’re trying to reinforce to, as soon as you can understand the state machines, you can have something that’s “checkable”; that’s correct. So I think programming, you know, which is weird to say, that the machine will beat us at most programming tasks before we let it take over roles that have deep empathy, you know, physical presence and social understanding in them.  LEE: Yeah. By the way, you know, I fully expect in five years that AI will produce mathematical proofs that are checkable for validity, easily checkable, because they’ll be written in a proof-checking language like Lean or something but will be so complex that no human mathematician can understand them. I expect that to happen.   I can imagine in some fields, like cellular biology, we could have the same situation in the future because the molecular pathways, the chemistry, biochemistry of human cells or living cells is as complex as any mathematics, and so it seems possible that we may be in a state where in wet lab, we see, Oh yeah, this actually works, but no one can understand why.  BUBECK: Yeah, absolutely. I mean, I think I really agree with Bill’s distinction of the discovery and the delivery, and indeed, the discovery’s when you can check things, and at the end, there is an artifact that you can verify. You know, you can run the protocol in the wet lab and see [if you have] produced what you wanted. So I absolutely agree with that.   And in fact, you know, we don’t have to talk five years from now. I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini (opens in new tab). So this is really amazing. And, you know, just very quickly, just so people know, it was about this statistical physics model, the frustrated Potts model, which has to do with coloring, and basically, the case of three colors, like, more than two colors was open for a long time, and o3 was able to reduce the case of three colors to two colors.   LEE: Yeah.  BUBECK: Which is just, like, astounding. And this is not … this is now. This is happening right now. So this is something that I personally didn’t expect it would happen so quickly, and it’s due to those reasoning models.   Now, on the delivery side, I would add something more to it for the reason why doctors and, in fact, lawyers and coders will remain for a long time, and it’s because we still don’t understand how those models generalize. Like, at the end of the day, we are not able to tell you when they are confronted with a really new, novel situation, whether they will work or not.  Nobody is able to give you that guarantee. And I think until we understand this generalization better, we’re not going to be willing to just let the system in the wild without human supervision.  LEE: But don’t human doctors, human specialists … so, for example, a cardiologist sees a patient in a certain way that a nephrologist …  BUBECK: Yeah. LEE: … or an endocrinologist might not. BUBECK: That’s right. But another cardiologist will understand and, kind of, expect a certain level of generalization from their peer. And this, we just don’t have it with AI models. Now, of course, you’re exactly right. That generalization is also hard for humans. Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know. LEE: OK. You know, the podcast is focused on what’s happened over the last two years. But now, I’d like one provocative prediction about what you think the world of AI and medicine is going to be at some point in the future. You pick your timeframe. I don’t care if it’s two years or 20 years from now, but, you know, what do you think will be different about AI in medicine in that future than today?  BUBECK: Yeah, I think the deployment is going to accelerate soon. Like, we’re really not missing very much. There is this enormous capability overhang. Like, even if progress completely stopped, with current systems, we can do a lot more than what we’re doing right now. So I think this will … this has to be realized, you know, sooner rather than later.  And I think it’s probably dependent on these benchmarks and proper evaluation and tying this with regulation. So these are things that take time in human society and for good reason. But now we already are at two years; you know, give it another two years and it should be really …   LEE: Will AI prescribe your medicines? Write your prescriptions?  BUBECK: I think yes. I think yes.  LEE: OK. Bill?  GATES: Well, I think the next two years, we’ll have massive pilots, and so the amount of use of the AI, still in a copilot-type mode, you know, we should get millions of patient visits, you know, both in general medicine and in the mental health side, as well. And I think that’s going to build up both the data and the confidence to give the AI some additional autonomy. You know, are you going to let it talk to you at night when you’re panicked about your mental health with some ability to escalate? And, you know, I’ve gone so far as to tell politicians with national health systems that if they deploy AI appropriately, that the quality of care, the overload of the doctors, the improvement in the economics will be enough that their voters will be stunned because they just don’t expect this, and, you know, they could be reelected [LAUGHTER] just on this one thing of fixing what is a very overloaded and economically challenged health system in these rich countries.  You know, my personal role is going to be to make sure that in the poorer countries, there isn’t some lag; in fact, in many cases, that we’ll be more aggressive because, you know, we’re comparing to having no access to doctors at all. And, you know, so I think whether it’s India or Africa, there’ll be lessons that are globally valuable because we need medical intelligence. And, you know, thank god AI is going to provide a lot of that.  LEE: Well, on that optimistic note, I think that’s a good way to end. Bill, Seb, really appreciate all of this.   I think the most fundamental prediction we made in the book is that AI would actually find its way into the practice of medicine, and I think that that at least has come true, maybe in different ways than we expected, but it’s come true, and I think it’ll only accelerate from here. So thanks again, both of you.  [TRANSITION MUSIC]  GATES: Yeah. Thanks, you guys.  BUBECK: Thank you, Peter. Thanks, Bill.  LEE: I just always feel such a sense of privilege to have a chance to interact and actually work with people like Bill and Sébastien.    With Bill, I’m always amazed at how practically minded he is. He’s really thinking about the nuts and bolts of what AI might be able to do for people, and his thoughts about underserved parts of the world, the idea that we might actually be able to empower people with access to expert medical knowledge, I think is both inspiring and amazing.   And then, Seb, Sébastien Bubeck, he’s just absolutely a brilliant mind. He has a really firm grip on the deep mathematics of artificial intelligence and brings that to bear in his research and development work. And where that mathematics takes him isn’t just into the nuts and bolts of algorithms but into philosophical questions about the nature of intelligence.   One of the things that Sébastien brought up was the state of evaluation of AI systems. And indeed, he was fairly critical in our conversation. But of course, the world of AI research and development is just moving so fast, and indeed, since we recorded our conversation, OpenAI, in fact, released a new evaluation metric that is directly relevant to medical applications, and that is something called HealthBench. And Microsoft Research also released a new evaluation approach or process called ADeLe.   HealthBench and ADeLe are examples of new approaches to evaluating AI models that are less about testing their knowledge and ability to pass multiple-choice exams and instead are evaluation approaches designed to assess how well AI models are able to complete tasks that actually arise every day in typical healthcare or biomedical research settings. These are examples of really important good work that speak to how well AI models work in the real world of healthcare and biomedical research and how well they can collaborate with human beings in those settings.  You know, I asked Bill and Seb to make some predictions about the future. You know, my own answer, I expect that we’re going to be able to use AI to change how we diagnose patients, change how we decide treatment options.   If you’re a doctor or a nurse and you encounter a patient, you’ll ask questions, do a physical exam, you know, call out for labs just like you do today, but then you’ll be able to engage with AI based on all of that data and just ask, you know, based on all the other people who have gone through the same experience, who have similar data, how were they diagnosed? How were they treated? What were their outcomes? And what does that mean for the patient I have right now? Some people call it the “patients like me” paradigm. And I think that’s going to become real because of AI within our lifetimes. That idea of really grounding the delivery in healthcare and medical practice through data and intelligence, I actually now don’t see any barriers to that future becoming real.  [THEME MUSIC]  I’d like to extend another big thank you to Bill and Sébastien for their time. And to our listeners, as always, it’s a pleasure to have you along for the ride. I hope you’ll join us for our remaining conversations, as well as a second coauthor roundtable with Carey and Zak.   Until next time.   [MUSIC FADES]

On November 4, 2003, a doctor gave Jon Gluck some of the worst news imaginable: He had cancer — one that later tests would reveal as multiple myeloma, a severe blood and bone marrow cancer. Jon was told he might have as little as 18 months to live. He was 38, a thriving magazine editor in New York with a 7-month-old daughter whose third birthday, he suddenly realized, he might never see.“The moment after I was told I had cancer, I just said ‘no, no, no,’” Jon told me in an interview just last week. “This cannot be true.”Living in remissionThe fact that Jon is still here, talking to me in 2025, tells you that things didn’t go the way the medical data would have predicted on that November morning. He has lived with his cancer, through waves of remission and recurrence, for more than 20 years, an experience he chronicles with grace and wit in his new book An Exercise in Uncertainty. That 7-month-old daughter is now in college.RelatedWhy do so many young people suddenly have cancer?You could say Jon has beaten the odds, and he’s well aware that chance played some role in his survival. (“Did you know that ‘Glück’ is German for ‘luck’?” he writes in the book, noting his good fortune that a random spill on the ice is what sent him to the doctor in the first place, enabling them to catch his cancer early.) Cancer is still a terrible health threat, one that is responsible for 1 in 6 deaths around the world, killing nearly 10 million people a year globally and over 600,000 people a year in the US. But Jon’s story and his survival demonstrate something that is too often missed: We’ve turned the tide in the war against cancer. The age-adjusted death rate in the US for cancer has declined by about a third since 1991, meaning people of a given age have about a third lower risk of dying from cancer than people of the same age more than three decades ago. That adds up to over 4 million fewer cancer deaths over that time period. Thanks to breakthroughs in treatments like autologous stem-cell harvesting and CAR-T therapy — breakthroughs Jon himself benefited from, often just in time — cancer isn’t the death sentence it once was.Our World in DataGetting better all the timeThere’s no doubt that just as the rise of smoking in the 20th century led to a major increase in cancer deaths, the equally sharp decline of tobacco use eventually led to a delayed decrease. Smoking is one of the most potent carcinogens in the world, and at the peak in the early 1960s, around 12 cigarettes were being sold per adult per day in the US. Take away the cigarettes and — after a delay of a couple of decades — lung cancer deaths drop in turn along with other non-cancer smoking-related deaths.But as Saloni Dattani wrote in a great piece earlier this year, even before the decline of smoking, death rates from non-lung cancers in the stomach and colon had begun to fall. Just as notably, death rates for childhood cancers — which for obvious reasons are not connected to smoking and tend to be caused by genetic mutations — have fallen significantly as well, declining sixfold since 1950. In the 1960s, for example, only around 10 percent of children diagnosed with acute lymphoblastic leukemia survived more than five years. Today it’s more than 90 percent. And the five-year survival rate for all cancers has risen from 49 percent in the mid-1970s to 69 percent in 2019. We’ve made strikes against the toughest of cancers, like Jon’s multiple myeloma. Around when Jon was diagnosed, the five-year survival rate was just 34 percent. Today it’s as high as 62 percent, and more and more people like Jon are living for decades. “There has been a revolution in cancer survival,” Jon told me. “Some illnesses now have far more successful therapies than others, but the gains are real.”Three cancer revolutions The dramatic bend in the curve of cancer deaths didn’t happen by accident — it’s the compound interest of three revolutions.While anti-smoking policy has been the single biggest lifesaver, other interventions have helped reduce people’s cancer risk. One of the biggest successes is the HPV vaccine. A study last year found that death rates of cervical cancer — which can be caused by HPV infections — in US women ages 20–39 had dropped 62 percent from 2012 to 2021, thanks largely to the spread of the vaccine. Other cancers have been linked to infections, and there is strong research indicating that vaccination can have positive effects on reducing cancer incidence. The next revolution is better and earlier screening. It’s generally true that the earlier cancer is caught, the better the chances of survival, as Jon’s own story shows. According to one study, incidences of late-stage colorectal cancer in Americans over 50 declined by a third between 2000 and 2010 in large part because rates of colonoscopies almost tripled in that same time period. And newer screening methods, often employing AI or using blood-based tests, could make preliminary screening simpler, less invasive and therefore more readily available. If 20th-century screening was about finding physical evidence of something wrong — the lump in the breast — 21st-century screening aims to find cancer before symptoms even arise.Most exciting of all are frontier developments in treating cancer, much of which can be tracked through Jon’s own experience. From drugs like lenalidomide and bortezomib in the 2000s, which helped double median myeloma survival, to the spread of monoclonal antibodies, real breakthroughs in treatments have meaningfully extended people’s lives — not just by months, but years.Perhaps the most promising development is CAR-T therapy, a form of immunotherapy. Rather than attempting to kill the cancer directly, immunotherapies turn a patient’s own T-cells into guided missiles. In a recent study of 97 patients with multiple myeloma, many of whom were facing hospice care, a third of those who received CAR-T therapy had no detectable cancer five years later. It was the kind of result that doctors rarely see. “CAR-T is mind-blowing — very science-fiction futuristic,” Jon told me. He underwent his own course of treatment with it in mid-2023 and writes that the experience, which put his cancer into a remission he’s still in, left him feeling “physically and metaphysically new.”A welcome uncertaintyWhile there are still more battles to be won in the war on cancer, and there are certain areas — like the rising rates of gastrointestinal cancers among younger people — where the story isn’t getting better, the future of cancer treatment is improving. For cancer patients like Jon, that can mean a new challenge — enduring the essential uncertainty that comes with living under a disease that’s controllable but which could always come back. But it sure beats the alternative.“I’ve come to trust so completely in my doctors and in these new developments,” he said. “I try to remain cautiously optimistic that my future will be much like the last 20 years.” And that’s more than he or anyone else could have hoped for nearly 22 years ago. A version of this story originally appeared in the Good News newsletter. Sign up here!See More: Health

There are concerns that more children are being diagnosed with ADHDImgorthand/Getty Images Is ADHD in children on the rise? An assessment of thousands of studies released since 2020 suggests, surprisingly, that the answer is no – but the researchers behind the work have expressed their frustration at the poor quality of data available, meaning that the true picture remains murky. “The best data we have suggests that there has been no meaningful increase in ADHD prevalence,” says Alex Martin at King’s College London, but that masks a larger problem, she says. “Most of the research is too biased to…

Project Render. Image credit: The Bentway The Bentway has announced its summer 2025 season of programming headlined by a new public art exhibition, Sun/Shade, a dance performance atop a large sand dune, and a city-wide installation of a moving forest. From now until October 5, Sun/Shade will explore the city’s changing relationship with these natural features. The art exhibition aims to bring together a mix of artists, designers, and researchers from Toronto and beyond to deploy natural light and shadow as creative tools, to reveal how new thinking about familiar resources can improve urban life. The Gardiner offers a canvas for the new art exhibition and experimentation. Stretching over 6.5 km, the elevated highway’s canopy provides the city with its largest continuous shadow, making the area a natural haven for those seeking sun and shade. This space will provide the benefits of both elements while also serving as a gathering place for community events, public art, recreation, and lively celebrations throughout the summer. “As extreme urban heat events rise in Toronto and cities everywhere, it’s even more vital to create accessible spaces where communities can maximize access to shade and stay protected from harsh light,” said Ilana Altman, co-executive director of The Bentway. “This season of programming explores how we must adapt to our changing climate and reshape our public spaces to balance the benefits of both sun and shade. Our creative collaborators are prompting us to recognize shade as an essential public resource and embrace sunlight as a creative collaborator.” Visitors will be able to experience the world premiere of Sand Flight from Norway choreographer Ingri Fiksdal and theatre director Jonas Corell Petersen, where eight dancers and a 50-person choir will descend on a massive sand dune exploring new traditions for changing climates. Carrying on the theme is Moving Forest by Amsterdam’s NL Architects, a mobile project featuring a flock of 50 trees travelling throughout the city, will bring shade to sunbaked urban sites across the Greater Toronto Area (GTA). A key dimension of Sun/Shade explores the importance of shaded spaces for public health, anchored by a new partnership with The Bentway’s official Sun-Safety Partner, the David Cornfield Melanoma Fund. “Skin cancer is the most commonly diagnosed cancer in Canada, and the incidence of melanoma – the deadliest form of skin cancer – is rising. The best way to prevent skin cancer is to protect your skin from the sun,” said Danielle Paterson, executive director of the David Cornfield Melanoma Fund. “We are proud to partner with The Bentway on Sun/Shade, an amazing sun safety initiative that emphasizes the importance of accessible public shade in Toronto.” For more information, click here. The post Sun and shade take centre stage with The Bentway’s immersive summer program appeared first on Canadian Architect.

Unlike the other big streamers, Tubi only has a handful of original shows, most of them imports (their original movie selection is much larger). That's not to say it's a wasteland for TV addicts: The streamer might actually have too many shows, a vast and sometimes wild catalog that spans decades. As the likes of Netflix and HBO Max have slimmed down their catalogues, Tubi is growing, offering a mix of established hits, underrated gems, and more obscure offerings. For the sheer breadth of material on offer, it has become the first place I look for anything outside the current zeitgeist—like the following 18 shows, an entirely non-comprehensive sampling of what Tubi has to offer, crossing genres and decades.Gossip Girl (2007 – 2012) Occasionally referred to as the greatest teen drama of all time (certainly this side of 90210), Gossip Girl was a buzzy ratings champ for the CW back in the day, with its juicy, often scandalous storylines that veered so often into intentional satire that it was hard to ever get mad at the ridiculousness of any of it. Set among a group of well-heeled students on Manhattan's Upper East Side, its characters find their private lives being chronicled by the title’s mysterious master of gossip—so think of it as a proto-Bridgerton. You can stream Gossip Girl here.Babylon 5 (1993 – 1998, five seasons) J. Michael Straczynski’s wildly ambitious sci-fi epic was way ahead of its time, with a planned (more or less) five season story arc set on the titular space station. Babylon 5 is a remote outpost that becomes the last best hope for peace in the face of conflicting human and alien agendas—even more so after an ancient threat is awakened. With increasingly complex storylines that expanded over its run, this was a stab at prestige TV before that was a thing, and it still holds up (dated CGI effects notwithstanding. You can stream Babylon 5 here.Empire (2015 – 2020) Hip hop mogul and Empire Entertainment CEO Lucious Lyon (Terrence Howard) is dying, having been diagnosed with ALS at a young age. He wasn't planning to have to hand off his company so early, but nevertheless finds himself preparing his three very different sons (Trai Byers, Jussie Smollett, and Bryshere Y. Gray) to take the keys to the kingdom—by pitting them against one other. Into this already Shakespearean setup steps Lucious' ex-wife Cookie (Taraji P. Henson), just released from prison and harboring her own plans for Lucious's empire. You can stream Empire here. Mr. Robot (2015 – 2019) Social anxiety disorder, clinical depression, and dissociative identity disorder make up the potent blend of neurodivergences challenging Elliot Alderson (Rami Malek), a genius senior cybersecurity engineer at Allsafe Cybersecurity. In season one, he's recruited by an anarchist who goes by the moniker Mr. Robot (Christian Slater) to encrypt all the financial data of a global mega-conglomerate, thereby erasing massive amounts of debt (hey, real-life hackers, maybe take some notes?). The show starts strong and gets better across its increasingly labyrinthian four seasons—utterly preposterous while also feeling realistic in its technical detail. You can stream Mr. Robot here. Boarders (2024 - , two seasons) This British import feels a bit like a latter-day Skins, with a talented cast of young stars-in-waiting (including leads Josh Tedeku and Jodie Campbell) and a scholastic setting. At the (fictional) prestigious boarding school St. Gilbert’s, five Black teens are newly attending, having earned scholarships, but their integration into the existing cliques is less than smooth. The blend of coming-of-age drama with a willingness to take the piss when it comes to the whole rich private school thing makes this Tubi original a good time. You can stream Boarders here.Big Mood (2024 – , renewed for a second season) Another UK import and Tubi original (at least stateside), Big Mood stars Nicola Coughlan (Bridgerton) and Lydia West (It's a Sin) as a couple of besties in East London, living their best millennial thirtysomething lives. Well, kind of: Maggie's dealing with bipolar disorder, and unclear on whether she wants to continue with her medication as she sets out to write a play, while Lydia is doing her very best running a tanking dive bar inherited from her father. It's both a cute dramedy and an impressively frank exploration of the challenges of living with mental illness. You can stream Big Mood here. Vicious (2013 – 2016, two seasons) The old-school sitcom formula has never been executed quite this bitchily, with the inspired pairing of Ian McKellen and Derek Jacobi as Freddie Thornhill and Stuart Bixby, a couple of nearly 50 years who’ve developed a love-hate relationship. This cast, which includes Frances de la Tour and Game of Thrones’ Ian Rheon, is unbeatable, and the one-liners are hilariously nasty. You can stream Vicious here.The Haves and the Have Nots (2013 – 2021, eight seasons) Tyler Perry's old-school primetime soap was the show that practically built OWN; it was the then-new network's first scripted show, and an immediate breakout. It follows three families: The wealthy Harringtons and the Cryers are wealthy movers in Atlanta, Georgia, while the Young family is overseen by single mom Hanna, who's both a maid for the Cryers and confidante to the family matriarch. There's juicy tension galore between the three families, in no small part because of class differences, but also because they're all equally screwed. You can stream The Haves and the Have Nots here. Spartacus (2010 – 2013) Doing Ridley Scott’s Gladiator one better in terms of both narrative complexity and in hot shirtless gay arena action, Spartacus starts off as pure spectacle and grows into a juicy, high-gloss soap opera by series' end. Buoyed by performances from leads Andy Whitfield (who tragically passed away during the series' original run), Manu Bennett, John Hannah, and Lucy Lawless, it’s sword-and-sandals done right. A follow-up series is in development over at Starz, so it's a good time to catch up. You can stream Spartacus here. Broadchurch (2013 – 2017) Creator Chris Chibnall's dark crime drama didn't invent its particular sub-genre (whatever you call the one where two troubled homicide detectives butt heads in a gloomy town), but it did popularize it to the point that we've been inundated with countless imitators of wide-ranging quality. With the great pairing of Olivia Colman and David Tennant (joined by yet another Doctor Who Doctor, Jodie Whittaker), Broadchurch still stands alongside the best of its kind. You can stream Broadchurch here.Doctor Who (1963 – 1989, 26 seasons) Speaking of Doctor Who, even if you're current with the modern incarnation (if I can use "modern" for a show that started airing in 2005), you've got a lot of timey-wimey adventures to enjoy. Tubi has the entirity of the surviving 26-season original run, going all the way back to 1963 and the story of a mysterious old man living in a junkyard with his granddaughter. Seven doctors is enough to keep anyone busy for a while. Tubi has the show broken out by Doctor, but, if you want to start from the beginning you can stream The First Doctor here. Haven (2010 – 2015) Tubi is a haven for small gems like this, a five-season Stephen King adaptation originally produced by SyFy. Emily Rose stars as Audrey Parker, and FBI Special Agent sent to the small town of Haven, Maine on a routine case who gets drawn into “The Troubles," a series of harmful supernatural events that have recurred throughout the town’s history. A supernatural-case-of-the-week format gives way to a bigger mystery when Audrey comes to learn that this isn’t her first time in Haven, nor the first time she’s encountered the Troubles. You can stream Haven here.Scandal (2012 – 2018, seven seasons) Shonda Rhimes was already a powerhouse producer and screenwriter with several successful seasons of Grey's Anatomy under her belt when Scandal debuted, but its blend of political thrills and sexy, soapy drama is what solidified her brand, and her spot atop of the modern TV landscape. Kerry Washington stars as Olivia Pope, head of the DC-based crisis management firm Olivia Pope & Associates (OPA), who is the person to call when you've got a PR disaster to fix. If you want to get a sense of the stakes involved, consider that Tony Goldwyn costars as Fitzgerald Grant III, president of the United States, and also Olivia's lover. You can stream Scandal here. Buffy the Vampire Slayer (1997 – 2003) With word that Sarah Michelle Gellar (and company?) are returning to the wreckage of Sunnydale for a Hulu reboot, it’s probably not a bad time to visit (or revisit, or re-revisit) this seven-season teen vampire hunter saga. While the pacing might feel a little slow, and the effects a little janky, its blend of high school (and then college) angst, kick-ass monster fights, and genuinely laugh-out-loud comedy holds up. You can stream Buffy here.Heartland (2007 – , 18 seasons) If there’s a stereotype that middle-American viewers won’t watch foreign fare, this show puts the lie to it—at least when it comes to imports from Alberta (tariff-free!). Based on a popular book series from Linda Chapman and Beth Chambers (writing under the name Lauren Brooke), the show follows the lives of a family of horse ranchers in western Canada, led by sisters Amy and Lou (Amber Marshall and Michelle Morgan). Tubi currently has only the first 15 seasons of the drama, which has recently been renewed for a 19th. That’s Law & Order-level longevity, people. You can stream Heartland here.Highlander (1992 – 1998, six seasons) An classic of '90s-era syndicated action/adventure, Highlander stars Adrian Paul as the title hero, taking over from Christopher Lambert in the film series. Duncan MacLeod is an immortal warrior living in the modern(-ish) day, hunted by others of his own kind, whose goal is singular: to chop off Duncan's head in order to steal his power. Episodes typically involve some sort of flashback to an earlier era in Duncan's life where we first encounter the threat he'll face in the modern day. There's at least one good sword fight in every episode, and I can't imagine what more you'd want out of a series. Bonus: It carries over the films' kick-ass Queen theme song. You can stream Highlander here. Z Nation (2014 - 2019) The Walking Dead made prestige television out of the zombie apocalypse, but this SyFy channel original is all about zombies as a campy, gory good time.  Things kick off with a soldier who’s been tasked with transporting a package across country. The package in question is actually a human being, the survivor of a zombie bite who might be able to help create a vaccine (take note, The Last of Us fans). This one comes from the schlock-masters at The Asylum, purveyors of infamous B-movies like Sharknado, which should tell you all you need to know about the tone. You can stream Z Nation here.Columbo (1968 – 2003, 16 seasons) Peter Falk's sublimely rumpled detective practically invented the style that Peacock's Poker Face has recently revived: a crime (usually a murder) is committed, the viewers know whodunnit, and Columbo has to solve it. Early on in any given episode, we get to watch the crime being committed, though we don't always know the motive. The challenge isn't to figure out the culprit, but to discover exactly how TV's greatest detective is going to solve the case. You can stream Columbo here.

Tumor Has ItJun 1, 10:00 AM EDT / by Noor Al-Sibai400 Women Are Suing Pfizer Over Birth Control Shot That Allegedly Gave Them Brain TumorsThe pharmaceutical giant allegedly knew about the risks... but didn't warn patients.Jun 1, 10:00 AM EDT / Noor Al-SibaiImage by Beata Zawrzel / NurPhoto via Getty / FuturismRx/MedicinesRecent research has linked Pfizer's widely-used Depo-Provera birth control shot to massively increased risk of developing brain tumors — and hundreds of women are suing the pharmaceutical giant over it.According to a press release filed on behalf of the roughly 400 plaintiffs in the class action suit, the lawsuit claims that Pfizer and other companies that made generic versions of the injectable contraceptive knew of the link between the shot and the dangerous tumors, but didn't properly warn users.The suit follows a study published by the British Medical Journal last year that found that people who took the progestin-based shot for a year or more were up to 5.6 times more likely to develop meningioma, a slow-building brain tumor that forms, per the Cleveland Clinic, on the meninges, or layers of tissue that covers the brain and spinal cord.Though Pfizer attached warning labels about meningioma to Depo-Provera sold in Canada in 2015 and the UK, Europe, and South Africa after the 2024 study was published, no such label was deployed in the United States — a failure which according to the lawsuit is "inconsistent [with] global safety standards."In an interview with the website DrugWatch, one of the suit's plaintiffs, who was identified by the initials TC, said that she had been "told how great Depo-Provera was" and decided to start it after an unplanned pregnancy that occurred when she'd taken the since-discontinued birth control pill Ortho Tri-Cyclen Lo."I thought it would be more reliable and convenient since I wouldn’t have to take it daily," TC told the site, referencing the four annual injections Depo-Provera requires. "I had no idea it would lead to such serious health problems."After being on the contraceptive shot for three years — and experiencing intense headaches, months-long uterine bleeding, and weight gain — the woman finally consulted her doctor and was diagnosed with meningioma. She's since been undergoing treatment and experienced some relief, but even that experience has been "physically and emotionally draining" because she has to get regular MRIs to monitor the tumor, which likely isn't fatal but still greatly affects her quality of life."It’s a constant worry that the tumor might grow," TC said, "and the appointments feel never-ending."That fear was echoed by others who spoke to the Daily Mail about their meningioma diagnoses after taking Depo-Provera. Unlike TC, Andrea Faulks of Alabama hadn't been on the shots for years when she learned of her brain tumors, which caused her years of anguish.Faulks told the British website that she'd begun taking the medication back in 1993, the year after it was approved by the FDA in the United States. She stopped taking it only a few years later, but spent decades having splitting headaches and experiencing dizziness and tremors. After being dismissed by no fewer than six doctors, the woman finally got an MRI last summer and learned that she had a brain tumor — and is now undergoing radiation to shrink it after all this time."I know this is something I'm going to have to live with for the rest of my life, as long as I live," Faulks told the Daily Mail.Currently, the class action case against Pfizer on behalf of women like Faulks and TC is in its earliest stages as attorneys representing those hundreds of women with brain tumors start working to make them whole.Even if they receive adequate payouts, however, that money won't take away their suffering, or give them back the years of their life lost to tumors they should have been warned about.Share This ArticleImage by Beata Zawrzel / NurPhoto via Getty / FuturismRead This Next

Published May 31, 2025 6:00am EDT close Brain implant enables ALS patient to communicate using AI ALS patient communicates with the world using only his thoughts. Imagine losing your ability to speak or move, yet still having so much to say. For Brad G. Smith, this became his reality after being diagnosed with ALS, a rare and progressive disease that attacks the nerves controlling voluntary muscle movement. But thanks to a groundbreaking Neuralink brain implant, Smith is now able to communicate with the world using only his thoughts. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)Life before NeuralinkBefore receiving the Neuralink implant, Smith relied on eye-tracking technology to communicate. While impressive, it came with major limitations. "It is a miracle of technology, but it is frustrating. It works best in dark rooms, so I was basically Batman. I was stuck in a dark room," Smith shared in a recent post on X. Bright environments would disrupt the system, making communication slow and sometimes impossible. Now, Smith says, "Neuralink lets me go outside and ignore lighting changes."PARALYZED MAN WITH ALS IS THIRD TO RECEIVE NEURALINK IMPLANT, CAN TYPE WITH BRAIN ALS patient Brad G. Smith. (Bradford G. Smith/X)How the Neuralink brain implant worksSmith is the first non-verbal person and only the third individual worldwide to receive the Neuralink Brain-Computer Interface (BCI). The device, about as thick as five stacked coins, sits in his skull and connects to the motor cortex-the part of the brain that controls movement.Tiny wires, thinner than human hair, extend into Smith's brain. These pick up signals from his neurons and transmit them wirelessly to his MacBook Pro. The computer then decodes these signals, allowing Smith to move a cursor on the screen with his thoughts alone.As Smith explains, "The Neuralink implant embedded in my brain contains 1024 electrodes that capture neuron firings every 15 milliseconds generating a vast amount of data. Artificial intelligence processes this data on a connected MacBook Pro to decode my intended movements in real time to move the cursor on my screen. Neuralink does not read my deepest thoughts or words I think about. It just reads how I wanna move and moves the cursor where I want."WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Neuralink brain implant. (Bradford G. Smith/X)Training the brain-computer connectionLearning to use the system took some trial and error. At first, the team tried mapping Smith's hand movements to the cursor, but it didn't work well. After more research, they discovered that signals related to his tongue were the most effective for cursor movement, and clenching his jaw worked best for clicking. "I am not actively thinking about my tongue, just like you don't think about your wrist when you move a mouse. I have done a lot of cursor movements in my life. I think my brain has switched over to subconscious control quickly so I just think about moving the cursor," Smith said. ALS patient Brad G. Smith with his wife and child. (Bradford G. Smith/X)Everyday life: Communication, play, and problem-solvingThe Neuralink implant has given Smith new ways to interact with his family and the world. He can now play games like Mario Kart with his children and communicate more quickly than before. The system includes a virtual keyboard and shortcuts for common actions, making tasks like copying, pasting and navigating web pages much easier.Smith also worked with Neuralink engineers to develop a "parking spot" feature for the cursor. "Sometimes you just wanna park the cursor and watch a video. When it is in the parking spot, I can watch a show or take a nap without worrying about the cursor," he explained. ALS patient Brad G. Smith and his child. (Bradford G. Smith/X)AI assistance: Keeping up with conversationTo speed up communication even more, Smith uses Grok, Elon Musk's AI chatbot. Grok helps him write responses and even suggests witty replies. "We have created a chat app that uses AI to listen to the conversation and gives me options to say in response. It uses Grok 3 and an AI clone of my old voice to generate options for me to say. It is not perfect, but it keeps me in the conversation and it comes up with some great ideas," Smith shared. One example? When a friend needed a gift idea for his girlfriend who loves horses, the AI suggested a bouquet of carrots. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)The human side: Family, faith and perspectiveSmith's journey has been shaped by more than just technology. He credits his wife, Tiffany, as his "best caregiver I could ever imagine," and recognizes the support of his kids, friends and family. Despite the challenges of ALS, Smith finds meaning and hope in his faith. "I have not always understood why God afflicted me with ALS but with time I am learning to trust his plan for me. I'm a better man because of ALS. I'm a better disciple of Jesus Christ because of ALS. I'm closer to my amazing wife, literally and figuratively, because of ALS," he said. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)Looking ahead: What does this mean for others?Neuralink's technology is still in its early stages, but Smith's experience is already making waves. The company recently received a "breakthrough" designation from the Food and Drug Administration for its brain implant device, which hopes to help people with severe speech impairments caused by ALS, stroke, spinal cord injury and other neurological conditions.Neuro-ethicists are watching closely, as the merging of brain implants and AI raises important questions about privacy, autonomy and the future of human communication. ALS patient Brad G. Smith and his family. (Bradford G. Smith/X)Kurt's key takeawaysSmith's story is about resilience, creativity and the power of technology to restore something as fundamental as the ability to communicate. As Smith puts it,CLICK HERE TO GET THE FOX NEWS APPIf you or a family member lost the ability to speak or move, would you consider a brain implant that lets you communicate with your thoughts? Let us know by writing to us atCyberguy.com/ContactFor more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/NewsletterAsk Kurt a question or let us know what stories you'd like us to cover.Follow Kurt on his social channels:Answers to the most-asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com. All rights reserved.   Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.

Last Surviving Grandson of President John Tyler, Who Took Office in 1841, Dies at 96 When Harrison Ruffin Tyler’s grandfather was born 235 years ago in 1790, George Washington had just become the nation’s first president John Tyler was 63 when his 13th child was born in 1853. That child, Lyon Gardiner Tyler Sr., was 75 when Harrison Ruffin Tyler was born in 1928. Heritage Art / Heritage Images via Getty Images Harrison Ruffin Tyler, grandson of the tenth American president John Tyler, died on May 25 at age 96. Though the cause of death has not been revealed, his health had deteriorated in recent years. He had been diagnosed with dementia and suffered several small strokes starting in 2012, reports the New York Times’ Robert D. McFadden. He died at his home in a retirement community in Richmond, Virginia, according to the Washington Post’s Andrew Jeong and Brian Murphy. After the death of his brother, Lyon Gardiner Tyler Jr., in September 2020, Harrison Ruffin Tyler was the last surviving grandson of John Tyler, who was born in 1790 and led the nation between 1841 and 1845. But how could someone born in 1790 still have—until very recently—living grandchildren? Even the president’s grandson acknowledged that the time frame was difficult to comprehend. “When you talk about my grandfather born in the 1700s, there is a disconnect there,” he told WTVR’s Scott Wise and Greg McQuade in 2012. The unusual timeline was the result of second marriages and late-in-life fatherhood for the former president and, later, one of his sons. John Tyler was 63 when his 13th child, Lyon Gardiner Tyler Sr., was born in 1853. Then, Lyon Gardiner Tyler Sr. was 75 when Harrison Ruffin Tyler was born in 1928. “Both my grandfather—the president—and my father were married twice,” he told New York magazine’s Dan Amira in 2012. “And they had children by their first wives. And their first wives died, and they married again and had more children.” With so many relatives to keep track of, he added, “it does get very confusing.” “When I was a child, I did know most of the descendants, but as you get more generations down the line, it’s hard to keep track of everybody,” he said.John Tyler was born just after George Washington became the fledgling nation’s first president. He pursued a career in politics, serving as Virginia’s governor, as well as a United States representative and senator. He became America’s vice president when William Henry Harrison was elected president in 1840. When Harrison died of pneumonia a month into his term, John Tyler became the first vice president to succeed a president who died in office. His ascension was controversial, with some federal lawmakers questioning the legitimacy of his claims to the presidency. Some detractors even took to calling him “His Accidency.” The issue was not officially settled until 1967, with the ratification of the 25th Amendment. As president, one of John Tyler’s biggest accomplishments was pursuing the annexation of Texas, which officially joined the Union in 1845 under President James K. Polk. After his stint in the White House, he retired to his plantation on Virginia’s James River. During the Civil War, he was elected to the Confederate legislature, but he died in 1862 before he could take office. During his lifetime, he had a record-setting 15 children—the most of any U.S. president. He was married twice: first to Letitia Christian, who became the first president’s wife to die in the White House in 1842, followed by Julia Gardiner, who also served as First Lady. Their fifth child was Lyon Gardiner Tyler Sr., who later fathered Harrison Ruffin Tyler. Born in 1928, Harrison Ruffin Tyler studied chemistry at the College of William & Mary and chemical engineering at Virginia Tech before founding a water treatment company called ChemTreat. He and his wife, Frances Payne Bouknight, who died in 2019, also spent many years restoring the family’s ancestral home, Sherwood Forest Plantation. The 1,600-acre property, built around 1730 and purchased by his grandfather during his presidency, is a National Historic Landmark. The couple also worked to preserve a nearby Union supply depot called Fort Pocahontas that had been constructed by a regiment of Black soldiers during the Civil War. They had three children together: Harrison Ruffin Tyler Jr., William Bouknight Tyler and Julia Gardiner Tyler Samaniego. Harrison Ruffin Tyler “will be missed immeasurably by those who survive him,” Annique Dunning, the executive director of Sherwood Forest, said in a statement, as reported by the Associated Press. “He will be remembered for his considerable charm, generosity and unfailing good humor by all who knew him.” Get the latest stories in your inbox every weekday.

Let’s start with one unambiguous fact: More children are diagnosed with autism today than in the early 1990s. According to a sweeping 2000 analysis by the Centers for Disease Control and Prevention, a range of 2–7 per 1,000, or roughly 0.5 percent of US children, were diagnosed with autism in the 1990s. That figure has risen to 1 in 35 kids, or roughly 3 percent.The apparent rapid increase caught the attention of people like Robert F. Kennedy Jr., who assumed that something had to be changing in the environment to drive it. In 2005, Kennedy, a lawyer and environmental activist at the time, authored an infamous essay in Rolling Stone that primarily placed the blame for the increased prevalence of autism on vaccines. (The article was retracted in 2011 as more studies debunked the vaccine-autism connection.) More recently, he has theorized that a mysterious toxin introduced in the late 1980s must be responsible. Now, as the nation’s top health official leading the Department of Health and Human Services, Kennedy has declared autism an “epidemic.” And, in April, he launched a massive federal effort to find the culprit for the rise in autism rates, calling for researchers to examine a range of suspects: chemicals, molds, vaccines, and perhaps even ultrasounds given to pregnant mothers. “Genes don’t cause epidemics. You need an environmental toxin,” Kennedy said in April when announcing his department’s new autism research project. He argued that too much money had been put into genetic research — “a dead end,” in his words — and his project would be a correction to focus on environmental causes. “That’s where we’re going to find an answer.”But according to many autism scientists I spoke to for this story, Kennedy is looking in exactly the wrong place. Three takeaways from this storyExperts say the increase in US autism rates is mostly explained by the expanding definitions of the condition, as well as more awareness and more screening for it.Scientists have identified hundreds of genes that are associated with autism, building a convincing case that genetics are the most important driver of autism’s development — not, as Health Secretary Robert F. Kennedy Jr. has argued, a single environmental toxin.Researchers fear Kennedy’s fixation on outside toxins could distract from genetic research that has facilitated the development of exciting new therapies that could help those with profound autism.Autism is a complex disorder with a range of manifestations that has long defied simple explanations, and it’s unlikely that we will ever identify a single “cause” of autism.But scientists have learned a lot in the past 50 years, including identifying some of the most important risk factors. They are not, as Kennedy suggests, out in our environment. They are written into our genetics. What appeared to be a massive increase in autism was actually a byproduct of better screening and more awareness. “The way the HHS secretary has been walking about his plans, his goals, he starts out with this basic assumption that nothing worthwhile has been done,” Helen Tager-Flusberg, a psychologist at Boston University who has worked with and studied children with autism for years, said. “Genes play a significant role. We know now that autism runs in families… There is no single underlying factor. Looking for that holy grail is not the best approach.”Doctors who treat children with autism often talk about how they wish they could provide easy answers to the families. The answers being uncovered through genetics research may not be simple per se, but they are answers supported by science.Kennedy is muddying the story, pledging to find a silver-bullet answer where likely none exists. It’s a false promise — one that could cause more anxiety and confusion for the very families Kennedy says he wants to help. Robert F. Kennedy Jr. speaks during a news conference at the Department of Health and Human Services in mid-April to discuss this agency’s efforts to determine the cause of autism. Alex Wong/Getty ImagesThe autism “epidemic” that wasn’tAutism was first described in 1911, and for many decades, researchers and clinicians confused the social challenges and language development difficulties common among those with the condition for a psychological issue. Some child therapists even blamed the condition on bad parenting. But in 1977, a study discovered that identical twins, who share all of their DNA, were much more likely to both be autistic than fraternal twins, who share no more DNA than ordinary siblings. It marked a major breakthrough in autism research, and pushed scientists to begin coalescing around a different theory: There was a biological factor.At the time, this was just a theory — scientists lacked the technology to prove those suspicions at the genetic level. And clinicians were also still trying to work out an even more fundamental question: What exactly was autism? For a long time, the criteria for diagnosing a person with autism was strictly based on speech development. But clinicians were increasingly observing children who could acquire basic language skills but still struggled with social communication — things like misunderstanding nonverbal cues or taking figurative language literally. Psychologists gradually broadened their definition of autism from a strict and narrow focus on language, culminating in a 2013 criteria that included a wide range of social and emotional symptoms with three subtypes — the autism spectrum disorder we’re familiar with today.Along the way, autism had evolved from a niche diagnosis for the severely impaired to something that encompassed far more children. It makes sense then, that as the broad criteria for autism expanded, more and more children would meet it, and autism rates would rise. That’s precisely what happened. And it means that the “epidemic” that Kennedy and other activists have been fixated on is mostly a diagnostic mirage. Historical autism data is spotty and subject to these same historical biases, but if you look at the prevalence of profound autism alone — those who need the highest levels of support — a clearer picture emerges. (There is an ongoing debate in the autism community about whether to use the terminology of “profound autism” or “high support needs” for those who have the most severe form of the condition.) In the ’80s and ’90s, low-support needs individuals would have been less likely to receive an autism diagnosis given the more restrictive criteria and less overall awareness of the disorder, meaning that people with severe autism likely represented most of the roughly 0.5 percent of children diagnosed with autism in the 1990s. (One large analysis from Atlanta examining data from 1996 found that 68 percent of kids ages 3 to 10 diagnosed with autism had an IQ below 70, the typical cutoff for intellectual disability.)By 2025, when about 3 percent of children are being diagnosed with autism, about one in four of those diagnosed are considered to have high-support needs autism, those with most severe manifestation of the condition. That would equal about 0.8 percent of all US children — which would be a fairly marginal increase from autism rates 30 years ago. Or look at it another way: In 2000, as many as 60 percent of the people being diagnosed with autism had an intellectual disability, one of the best indicators of high-support needs autism. In 2022, that percentage was less than 40 percent.As a recently published CDC report on autism prevalence among young children concluded, the increase in autism rates can largely be accounted for by stronger surveillance and more awareness among providers and parents, rather than a novel toxin or some other external factor driving an increase in cases.Other known risk factors — like more people now having babies later in their life, given that parental age is linked to a higher likelihood of autism — are more likely to be a factor than anything Kennedy is pointing at, experts say. “It’s very clear it’s not going to be one environmental toxin,” said Alison Singer, founder of the Autism Science Foundation and parent of a child with profound autism. “If there were a smoking gun, I think they would have found it.”While Kennedy has fixated on vaccines and environmental influences, scientists have gained more precision in mapping human genetics and identifying the biological mechanisms that appear to be a primary cause of autism. And that not only helps us understand why autism develops, but potentially puts long-elusive therapies within reach. It began with an accident in the 1990s. Steven Scherer, now director of the Center for Applied Genomics at the Hospital for Sick Children in Toronto, began his career in the late 1980s trying to identify the gene that caused cystic fibrosis — in collaboration with Francis Collins, who went on to lead the Human Genome Project that successfully sequenced all of the DNA in the human genome in the early 2000s. Scherer and Collins’s teams focused on chromosome 7, identified as a likely target by the primitive genetic research available at the time, a coincidence that would reorient Scherer’s career just a few years later, putting him on the trail of autism’s genetic roots.After four years, the researchers concluded that one gene within chromosome 7 caused cystic fibrosis. Soon after Scherer helped crack the code on cystic fibrosis in the mid-1990s, two parents from California called him: He was the world’s leading expert on chromosome 7, and recent tests had revealed that their children with autism had a problem within that particular chromosome.That very same week, Scherer says, he read the findings of a study by a group at Oxford University, which had looked at the chromosomes of families with two or more kids with autism. They, too, had identified problems within chromosome 7.“So I said, ‘Okay, we’re going to work on autism,’” Scherer told me. He helped coordinate a global research project, uniting his Canadian lab with the Oxford team and groups in the US to run a database that became the Autism Genome Project, still the world’s largest repository of genetic information of people with autism.They had a starting point — one chromosome — but a given chromosome contains hundreds of genes. And humans have, of course, 45 other chromosomes, any of which conceivably might play a role. So over the years, they collected DNA samples from thousands upon thousands of people with autism, sequenced their genes, and then searched for patterns. If the same gene is mutated or missing across a high percentage of autistic people, it goes on the list as potentially associated with the condition. Scientists discovered that autism has not one genetic factor, but many — further evidence that this is a condition of complex origin, in which multiple variables likely play a role in its development, rather than one caused by a single genetic error like sickle-cell anemia.Here is one way to think about how far we have come: Joseph Buxbaum, the director of the Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai in New York, entered autism genetics research 35 years ago. He recalls scientists being hopeful that they might identify a half dozen or so genes linked to autism.They have now found 500 genes — and Buxbaum told me he believed they might find a thousand before they are through. These genetic factors continue to prove their value in predicting the onset of autism: Scherer pointed to one recent study in which the researchers identified people who all shared a mutation in the SHANK3 gene, one of the first to be associated with autism, but who were otherwise unalike: They were not related and came from different demographic backgrounds. Nevertheless, they had all been diagnosed with autism.Researchers analyze the brain activity of a 14-year-old boy with autism as part of a University of California San Francisco study that involves intensive brain imaging of kids and their parents who have a rare chromosome disruption connected to autism. The study, the Simons Variation in Individuals Project, is a genetics-first approach to studying autism spectrum and related neurodevelopmental disorders. Michael Macor/San Francisco Chronicle via The Associated PressPrecisely how much genetics contributes to the development of autism remains the subject of ongoing study. By analyzing millions of children with autism and their parents for patterns in diagnoses, multiple studies have attributed about 80 percent of a person’s risk of developing autism to their inherited genetic factors. But of course 80 percent is not 100 percent. We don’t yet have the full picture of how or why autism develops. Among identical twins, for example, studies have found that in most cases, if one twin has high-support needs autism, the other does as well, affirming the genetic effect. But there are consistently a small minority of cases — 5 and 10 percent of twin pairs, Scherer told me — in which one twin has relatively low-support needs while the one requires a a high degree of support for their autism.Kennedy is not wholly incorrect to look at environmental factors — researchers theorize that autism may be the result of a complex interaction between a person’s genetics and something they experience in utero. Scientists in autism research are exploring the possible influence when, for example, a person’s mother develops maternal diabetes, high blood sugar that persists throughout pregnancy. And yet even if these other factors do play some role, the researchers I spoke to agree that genetics is, based on what we know now, far and away the most important driver.“We need to figure out how other types of genetics and also environmental factors affect autism’s development,” Scherer said. “There could be environmental changes…involved in some people, but it’s going to be based on their genetics and the pathways that lead them to be susceptible.”While the precise contours of Health Department’s new autism research project is still taking shape, Kennedy has that researchers at the National Institutes of Health will collect data from federal programs such as Medicare and Medicaid and somehow use that information to identify possible environmental exposures that lead to autism. He initially pledged results by September, a timeline that, as outside experts pointed out, may be too fast to allow for a thorough and thoughtful review of the research literature. Kennedy has since backed off on that deadline, promising some initial findings in the fall but with more to come next year.RFK Jr.’s autism commission research risks the accessibility of groundbreaking autism treatmentsIf Kennedy were serious about moving autism science forward, he would be talking more about genetics, not dismissing them. That’s because genetics is where all of the exciting drug development is currently happening.A biotech firm called Jaguar Gene Therapy has received FDA approval to conduct the first clinical trial of a gene therapy for autism, focused on SHANK3. The treatment, developed in part by one of Buxbaum’s colleagues, is a one-time injection that would replace a mutated or missing SHANK3 gene with a functional one. The hope is that the therapy would improve speech and other symptoms among people with high-needs autism who have also been diagnosed with a rare chromosomal deletion disorder called Phelan-McDermid syndrome; many people with this condition also have Autism spectrum disorder.The trial will begin this year with a few infant patients, 2 years old and younger, who have been diagnosed with autism. Jaguar eventually aims to test the therapy on adults over 18 with autism in the future. Patients are supposed to start enrolling this year in the trial, which is focused on first establishing the treatment’s safety; if it proves safe, another round of trials would start to rigorously evaluate its effectiveness.“This is the stuff that three or four years ago sounded like science fiction,” Singer said. “The conversation has really changed from Is this possible? to What are the best methods to do it? And that’s based on genetics.”Researchers at Mount Sinai have also experimented with delivering lithium to patients and seeing if it improves their SHANK3 function. Other gene therapies targeting other genes are in earlier stages of development. Some investigators are experimenting with CRISPR technology, the revolutionary new platform for gene editing, to target the problematic genes that correspond to the onset of autism.But these scientists fear that their work could be slowed by Kennedy’s insistence on hunting for environmental toxins, if federal dollars are instead shifted into his new project. They are already trying to subsist amid deep budget cuts across the many funding streams that support the institutions where they work. “Now we have this massive disruption where instead of doing really key experiments, people are worrying about paying their bills and laying off their staff and things,” Scherer said. “It’s horrible.” For the families of people with high-needs autism, Kennedy’s crusade has stirred conflicting emotions. Alison Singer, the leader of the Autism Science Foundation, is also the parent of a child with profound autism. When I spoke with her, I was struck by the bind that Kennedy’s rhetoric has put people like her and her family in. Singer told me profound autism has not received enough federal support in the past, as more emphasis was placed on individuals who have low support needs included in the expanding definitions of the disorder, and so she appreciates Kennedy giving voice to those families. She believes that he is sincerely empathetic toward their predicament and their feeling that the mainstream discussion about autism has for too long ignored their experiences in favor of patients with lower support needs. But she worries that his obsession with environmental factors will stymie the research that could yield breakthroughs for people like her child.“He feels for those families and genuinely wants to help them,” Singer said. “The problem is he is a data denier. You can’t be so entrenched in your beliefs that you can’t see the data right in front of you. That’s not science.”See More: