#309 ‒ AI in medicine: its potential to revolutionize disease prediction, diagnosis, and outcomes, causes for concern in medicine and beyond, and more | Isaac Kohane, M.D., Ph.D. - Peter Attia (2024)

Isaac “Zak” Kohane, a pioneering physician-scientist and chair of the Department of Biomedical Informatics at Harvard Medical School, has authored numerous papers and influential books on artificial intelligence (AI), including The AI Revolution in Medicine: GPT-4 and Beyond. In this episode, Zak explores the evolution of AI, from its early iterations to the current third generation, illuminating how it is transforming medicine today and unlocking astonishing possibilities for the future. He shares insights from his unconventional journey and early interactions with GPT-4, highlighting significant AI advancements in image-based medical specialties, early disease diagnosis, and the potential for autonomous robotic surgery. He also delves into the ethical concerns and regulatory challenges of AI, its potential to augment clinicians, and the broader implications of AI achieving human-like creativity and expertise.

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We discuss:

• Zak’s unconventional journey to becoming a pioneering physician-scientist, and his early interactions with GPT-4 [2:15];
• The evolution of AI from the earliest versions to today’s neural networks, and the shifting definitions of intelligence over time [8:00];
• How vast data sets, advanced neural networks, and powerful GPU technology have driven AI from its early limitations to achieving remarkable successes in medicine and other fields [19:00];
• An AI breakthrough in medicine: the ability to accurately recognize retinopathy [29:00];
• Third generation AI: how improvements in natural language processing significantly advanced AI capabilities [32:00];
• AI concerns and regulation: misuse by individuals, military applications, displacement of jobs, and potential existential concerns [37:30];
• How AI is enhancing image-based medical specialties like radiology [49:15];
• The use of AI by patients and doctors [55:45];
• The potential for AI to augment clinicians and address physician shortages [1:02:45];
• The potential for AI to revolutionize early diagnosis and prediction of diseases: Alzheimer’s disease, CVD, autism, and more [1:08:00];
• The future of AI in healthcare: integration of patient data, improved diagnostics, and the challenges of data accessibility and regulatory compliance [1:17:00];
• The future of autonomous robotic surgery [1:25:00];
• AI and the future of mental health care [1:31:30];
• How AI may transform and disrupt the medical industry: new business models and the potential resistance from established medical institutions [1:34:45];
• Potential positive and negative impacts of AI outside of medicine over the next decade [1:38:30];
• The implications of AI achieving a level of creativity and expertise comparable to exceptional human talents [1:42:00];
• Digital immortality and legacy: the potential to emulate an individual’s personality and responses and the ethical questions surrounding it [1:45:45];
• Parting thoughts [1:50:15]; and
• More.

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Zak’s unconventional journey to becoming a pioneering physician-scientist, and his early interactions with GPT-4 [2:15]

Give folks a sense of your background. Your path through medical school and training was not very typical

• Zak grew up in Switzerland
• Nobody in his family was a doctor
• He came to the US, decided to major in biology, and then got nerd sniped by computing in the late 70s, so he minored in computer science
• He goes to medical school, and in the middle of the first year he realized it was not what he expected
  • It’s a noble profession, but it’s not a science (it’s an art)
  • He thought he was going into science
• He bails out for a while to do a PhD in computer science
  • This is in the early 80s, and it’s a heyday of AI (actually a 2nd heyday)
    • We’re currently in the 3rd heyday
  • It was a time of great promise
  • With retrospective scope, it’s very clear that it was not going to be successful
    • But unlike today, we had not released it to the public
    • It was not actually working in the way that we thought it was going to work, and it certainly didn’t scale
• His thesis advisor at MIT (Peter Szolovits) said, “Zak, you should finish your clinical training because I’m not getting a lot of respect from clinicians. And so, to bring rational decision making to the clinic, you really want to finish your clinical training.”
• Zak finished medical school, did a residency in pediatrics and then pediatric endocrinology, which was actually extremely enjoyable
• When he was done, he restarted his research in computing
• He started a lab at Children’s Hospital in Boston and then a center of biomedical informatics at the medical school
• Zak was getting a lot of grants, and like almost every other endeavor, getting money gets attention from the powers that be
• They asked him to start a center and then eventually a new department of biomedical informatics that he’s the chair of
  • They now have 16 professors or assistant professors of biomedical informatics
• Zak has been involved in a lot of machine learning projects

Like everybody else, Zak was taken by surprise by large language models

• He got an email from Peter Lee in October ‘22 (it was right out of a Michael Crichton novel), that said, “Zak, if you’ll answer the phone, I can’t tell you what it’s about, but it’ll be well worth your while.”
  • Peter was a Professor of Computer Science at CMU and also department chair there
  • Then, he went to DARPA and then he went to Microsoft
  • He told Zak about GPT-4, and this was before any of us had heard about ChatGPT (which is initially GPT-3.5)
  • He gets Zak early access to it when no one else knows that it exists, and Zak starts trying it against hard cases
• Zak remembers from his training, being called down to the nursery
  • There’s a child with a small phallus and a hole at the base of the phallus, and they can’t palpate testicl*s
  • They want to know what to do because Zak is a pediatric endocrinologist

So Zak asked GPT-4 about this case, “What would you do? What are you thinking about?”

• It runs him through the whole workup of these very rare cases of ambiguous genitalia
• In this case, it was congenital adrenal hyperplasia where the making of excess androgens during pregnancy and then subsequently birth causes the cl*tor*s to swell, form the glans of the penis, of the phallus, and the labia minora to fuse to form the shaft of what looks like a penis
  • But there’s no testicl*s, there’s ovaries
• There’s a whole endocrine workup with genetic tests, hormonal tests, ultrasound, and it does it all
• Zak explains, “It really blows my mind, because very few of us in computer science really thought that these large language models would scale up the way they do. It was just not expected.”
• Talking to Bill Gates about this, he told me that his line engineers in Microsoft Research, a lot of his fanciest computer scientists did not expect this
  • But the line engineers at Microsoft were just watching the scale-up, GPT-0, 1, 2, and they just saw it was going to keep on scaling up with the size of the data and with the size of the model
  • And they said, “Yeah, of course this is going to achieve this kind of expertise.”

“But the rest of us, I think because we value our own intellects so much, we couldn’t imagine how we would get that kind of conversational expertise just by scaling up the model and the data set.”‒ Zak Kohane

The evolution of AI from the earliest versions to today’s neural networks, and the shifting definitions of intelligence over time [8:00]

• Zak alluded to the fact that when he was doing his PhD in the early ‘80s, he was in the 2nd generation of AI
• This leads Peter to assume that the 1st generation was shortly following World War II
  • Alan Turing and the Turing test

Talk us through what Alan Turing posited, what the Turing test was and proposed to be, and really what the 1st generation of AI was

• After World War II, we had computing machines, and anybody who was a serious computer scientist could see that you could have these processes that could generate other processes

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