Comment by aclimatt | Hacker News Reader

Comment by aclimatt | Hacker News Reader

Unless existing physicians' associations put up a fight, which they absolutely will, AI replacing the diagnosis aspect of a PCP is both probably one of the best applications of LLMs possible and one of the biggest impacts they can make for humanity.

PCPs are notorious for misdiagnoses, they're expensive, hasty, often times don't believe or listen to patients, and frequently just don't have all the data. This isn't necessarily their fault -- they're overworked and the medical industry isn't making things better -- but the reality is primariy care isn't working very well right now even in developed countries. Imagine in developing ones...

Diagnosing a patient is in many ways an expert system problem which computers are excellent at. Amassing the data from every medical textbook ever written, plus every study ever done, plus clinical conversations with patients and their medical history (the hardest part), and you have the best PCP ever made. Add a nurse to manage the physicality of it and one day connect data to the system to track lifestyle behaviors, sleep, and things people won't necessarily self-report, and you have something revolutionary. And AI is kinder and more compassionate, as the article said.

No wonder people have been trying to crack this nut for decades (albeit with minimal success). I hope the LLM revolution helps make another big round of progress.

> Diagnosing a patient is in many ways an expert system problem which computers are excellent at.

I'm admittedly not a doctor, but this doesn't really match my understanding at all–after my dad got sick a couple of years ago I developed a bit of a fascination with reading about the practice of medicine, which has largely changed my view from an engineer's perspective like this to one with much more nuance.

Diagnoses in general are not nearly as cut and dry as people would like to believe, and getting to them is not often as simple as being a function of X symptom and Y test result. Patients are often vague or simply not equipped to provide a perfect history, tests have ranges and associated error, as well as risks of their own, treatments have risks themselves that may interplay with myriad other life factors. In many situations there may not be a definitive diagnosis to be had at all.

I fully agree, it's a jungle out there with contradicting or outdated studies and and and. Humans aren't that perfect either though. Personal knowledge can get outdated too and not all keep themselves up to speed. A less that ideal test result can trigger a different test or can get dismissed as "not so bad". Correlations can go unnoticed. An annoying personality might get invited less often for checks. So really, there are some aspects where AI can increase the quality of the medical act. Indeed we are far from replacing it so I won't even bother thinking about it right now, I mean me as a patient. But extra help? Please bring more.

Wasn't medical diagnosis also a goal of IBM Watson? I was honestly hoping to see real applications for that system but in the end it seems all we got was a robot that was pretty good at Jeopardy.

My understanding is that the jeopardy team got poached right away, so "Watson" was essentially just a trademark after that

Side question: did Watson also use LLMs as its core technology?

LLMs came some years after Watson- GPT 1 came out in 2018 (and was completely useless towards this goal), and Watson was developed mostly between 2005 and 2013

Ok, I suppose they might still have used the idea of a language model (LM) which has existed for much longer (Wikipedia says 1980). But the only difference would then be the use of transformers, which I understand is what the "L(arge)" refers to.

Side note. The terminology seems a bit confusing. Wikipedia says "LLMs are artificial neural networks following a transformer architecture." It's a bit strange to call it LLM then and not "Transformer-LM", imho.

If you take a dense (fully connected) neural network and take away edges, you can end up at the transformer architecture. Perhaps IBM just used fully connected networks and an insane amount of computational power and used the transformers without even knowing it (?)

I've had some discussions about AI in healthcare with a friend who is a MD and my interpretation of that is that initial face to face/spoken diagnosis is far from the bottle neck of healthcare. Rather the issue is that it is kind of always possible to find more things to work on. More potential cancers or various ailments. And the issue is that all the care that comes after that stage is what costs money and need to be prioritized. Though I expect the exact way how that prioritization works can differ quite a lot between different systems.

The point here being that adding AI diagnostics might improve on the quality of diagnosis but it might also potentially derail healthcare to some degree if the AI system doesn't question weather an investigation or treatment is actually worth it and should be prioritized. Then again, it might also be possible to make it prioritize more consistently and fairly...

AI 2.0 combined with the insane number of health markers Apple Watches and the like can collect will definitely hit GPs hard since preventative care is one of the main things they do afaik.

Imagine never needing to take another blood test again or getting early warnings for potential cancer that would have cost thousands to obtain before...

Won’t replace but can certainly augment doctor’s work

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