Comment by A_D_E_P_T | Hacker News Reader

Comment by A_D_E_P_T | Hacker News Reader

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I get that you really disdain LLMs. But consider that a totally off-the-shelf, stock model is acing the medical licensing exam. It doesn't only perform better than human counterparts at the very peak of their ability (young, high-energy, immediately following extensive schooling and dedicated multidisciplinary study) it leaves them in the dust.

If you think that the test is simple or even text-only, here are some sample questions: https://www.usmle.org/sites/default/files/2021-10/Step_1_Sam...

> What the study says is ...

Surely you realize that they're not going to write, "AI is already capable of replacing family doctors," though that is the obvious implication.

And that's just a stock model. GPT-o1 via the API /w agentic RAG is a better doctor than >99% of working physicians. (By "doctor" I mean something like "medical oracle" -- ask a question, get a correct answer.) It's not yet quite as good at generating and testing hypotheses, but few doctors actually bother to do that.

As an aside, I quickly tested GPT-o1 by giving it question 95 on the sample test. I'm no doctor, but I've got extensive training in chemistry and biochem, and I'm not ashamed to admit that the question totally stumped me.

GPT-o1 gave the correct answer the first time around, and a very detailed explanation as to why all other potential answers must be false. A really remarkable performance, I think.

Now imagine it's an open-ended scenario, not multiple-choice. It would still come to the right conclusion and provide an accurate diagnosis.

LLM can only provide statistically most likely diagnosis based on the training data. If used by the experienced doctor LLM can be a valuable tool that saves time and maybe even increases accuracy in majority of cases. The problem is that LLMs will be used to replace experienced doctors and will be used as 100% accurate tools. This will result in experienced doctors becoming first rare and then non-existent and outcomes for patients will become increasingly unfavorable due to LLMs always producing a confident result even when it would be obviously wrong to an experienced doctor or even just a student.

it has nothing to do with disdain of LLMs, i'm an extensive user of warp (a very good LLM-based tool) and at my job we use them in depth in the software i build for summarization and other tasks that LLMs are generally considered good at. i spend a lot of time working with LLMs and find that, in some cases, they can be extremely useful, particularly when it comes to completing simple tasks in natural language.

i am also aware of their limitations and have a reasonable and realistic view of what they can currently do and where they are headed. i have seen many failure modes, i am familiar with patterns in their output, and i understand the boundaries of their comprehension, capabilities and understanding.

not buying into the current silicon valley money pit du jour and misunderstanding studies to validate that view does not equate to just being disdainful. i'm being realistic, because i understand what they do and how they work.

i'm not going to circle with you - you don't seem all that interested in engaging with the meat of anything i say to you, and instead just want to continue to try and rationalize your misunderstanding of the single study you found in support of your position, which is your right.

i feel ethically obligated to say, once again, that an LLM isn't a doctor and you should under no circumstances go to one for medical advice. you could really cause yourself some problems.

if you do so, that's on you. best of luck. incidentally i suspect someone has an awesome picture of a monkey at a steep discount you might be interested in.

It's far more than a single study, just one example of a very powerful development. The same phenomenon is also occurring in state bar exams, etc. And that one study is hardly misunderstood -- as you can verify for yourself.

> i understand the boundaries of their comprehension, capabilities and understanding.

It seems to me that you are quite far behind the current state of the art, and you apparently underestimate even stock GPT-o1, which is pretty old news.

I'm willing to place a friendly wager with you: Let's find a doctor who does online consultations and give him three questions selected at random from that sample test. These are diagnostic-type questions that well reflect what a country doctor would encounter in daily practice. We can leave the questions open-ended or give him the multiple-choice options. Then we put GPT-o1 to the same questions. I'd be very happy to bet that the LLM outperforms the doctor. I'd even place a secondary bet that the LLM answers all questions correctly and that the doctor answers less than two questions correctly.

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