Building AI for better healthcare — the OpenAI Podcast Ep. 14

Hello, I'm Andrew Main, and this is the OpenAI Podcast. Today, we're talking to Dr. Nate Gross, Head of Health, and Karan Singhal, who leads Health AI Research at OpenAI. We'll cover what went into training models to handle sensitive questions and how it's helping clinicians, patients, and healthcare systems.

We actually worked really closely with a, with a group, uh, a cohort of around two hundred and fifty physicians across every stage of, of generation of this data.

And we're starting to see medications that have been sitting on a shelf that all of a sudden AI has found ways for them to have direct value in, in, in patient lives.

How did you find your way into healthcare?

So, uh, what, what drew me to healthcare initially was, uh, health policy.

Mm-hmm.

Um, I was, was very interested. This was before the, the first Obama election. Uh, value-based care was first becoming a thing. Um, I, I started studying, uh, different ways to make healthcare more accessible to more people and then, um, e-eventually went to, uh, Emory-

Mm.

for medical school, and, and what, what drew me to that was, uh, a large, uh, public hospital, Grady Hospital, you know, to make sure that you're, you're taking advantage of every clinical hour you have.

So what kind of things were you doing?

So I was mostly pissing off the IT department.

[laughs]

Um, uh, when, when I was in medical school, uh, the newsfeed came out, the iPhone came out, uh, Twitter came out, uh, the App Store came out, and, and so comparing the technology that we had as doctors, which was fax machine, clipboard, paper binder, the beginnings of electronic health records, to like what my friends had or what the patients had in the waiting room was pretty profound.

So you come at it from the point of view as an AI researcher. Where did your interest in applying this to healthcare come from?

So I, I nerded out a lot when I was younger about things like philosophy of mind, and I thought a lot about, you know, intelligence and how far could intelligence go and could machines be intelligent. Um, and, um, a lot of those explorations took me towards, as I was learning about AI and starting to work on my first AI projects, thinking a lot about the ways in which AI could have a lot of impact on humanity in the future. And I thought something like-- I didn't, I didn't predict the future or how fast it would happen, but I thought something like AGI would happen within our lifetimes. So then once, once I had that conviction, I thought a lot about, you know, what are the ways in which I can have either positive impact and, and hopefully make that a really large upside for humanity, or think about the ways in which we could avoid downside. So since then, uh, in my career, I've been thinking a lot about both sides of that coin, thinking about that from the perspective as a safety researcher, which is part of my background, and then really some of that work on safety and privacy that I was working on previously, I started applying it in healthcare, and then I started being like, "Whoa, there is a really massive opportunity to think about the application of this technology, especially large language models in healthcare." And that's what took me to transitioning to it full-time, was just the size of that opportunity and the fact that I felt like the healthcare and clinical AI world was kind of not fully aware of that, of that gap. Um, and so I just thought it was kind of a really amazing opportunity and responsibility to, to bring us there.