Singhal: Augmenting telemedicine with LLMs is highly achievable within five years
Karan Singhal · No Priors Ep. 17 | With Karan Singhal · May 18, 2023 · at 39:12
Karan Singhal, Staff Software Engineer at Google Research working on Med-PaLM, discusses the five-year outlook for AI in healthcare.
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“I think augmenting telemedicine, I think is, is, is kind of a short-term opportunity that I think in the next five years is, is very achievable.”
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More from Karan Singhal
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“One thing we're seeing with MedPOM-II as we get closer to physician-level performance on medical question answering is that it's hard to tell the difference anymore. It's hard to tell the difference between different models. It's hard to tell the difference be…”
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“When we took a variation of POM, the FlanPOM model, which was, again, work from Jason Wei and team you know, this is an instruction to a model that's been trained to follow instructions better. You know, again, it was able to perform quite well out of the box,…”
Singhal: Fine-tuning outperforms prompt tuning when providing over 100 examples
“If you have three to five examples, let's say, then I would prompt it. If you have maybe 10 or 50 examples, it would either be prompt tuning or fine tuning. I think generally in that realm, prompt tuning and fine tuning perform similarly, and I would prefer pr…”
Singhal: Medical AI still lacks grounded evaluations within specific clinical workflows
“One thing that has been missing from our work so far is really Grounded evaluations in a specific use case in a workflow to show that there is a benefit both in terms of safety in the short term and in terms of kind of long-term patient outcomes as well.”
Singhal: Specialized AI models will assist radiologists in the near term
“I think where there might be more of a need for specialized models Is when it comes down to kind of higher stakes workflows, and I think that might look in the short term more like a physician's assistant. And so imagine, for example, an agent that can work wi…”
Singhal: Med-PaLM and Med-PaLM 2 were trained without patient health information
“Like for example, MedPOM and MedPOM-II are trained without any patient health information. They, they're just kind of taking all the knowledge of POM and POM-II and then just kind of Aligning them and making them behave in a certain way.”