[deaux]

Absolutely. It's very unfortunate that this post used the worst example possible of using LLMs for medical purposes.

General-purpose LLMs are _fantastic_ at medical diagnosis that do not involve imaging. I am completely convinced that given enough information and time, frontier models already outperform >90% of doctors on initial diagnosis of internal issues and suggesting medical tests to further reject or confirm the most likely theories. To the point where I'm eagerly waiting for the first hospital in the world that's willing to be open and honest about using them for that first step, and then proceeding from there. I'll be on a flight there as soon as one arrives.

At the same time, they're worse than useless at anything involving medical imaging. Asking them to interpret them is worse than trying to interpret them yourself as a layman. And you surely wouldn't interpret them yourself.

[throwaway2037]

    > General-purpose LLMs are _fantastic_ at medical diagnosis that do not involve imaging.

Can you share the reasons that you believe this?

    > At the same time, they're worse than useless at anything involving medical imaging.

What is special about medical imaging that makes AI/LLMs specifically bad?

[riahi]

You can see it in just this PDF report.

It's multiple things. It never shows the subscapularis in the way that people actually look the tendon. It hyper fixates on the axial when I find the sagittal much more useful for subscapularis.

Figure 7. There's an arrow pointing "to the acromial undersurface". The arrow is not pointed to that location.

Figure 5. "thin bursal fluid". This is within physiologic variation, but is calling bursitis.

It keeps bringing up irrelevant normal things like the shape of the coracromipal arch, I assume because lots of websites have information about that as a patient focused possible cause for rotator cuff impingement.

I am reminded of the recent Stanford MIRAGE study which found that LLMs will happily hallucinate answers about medical images if the medical images are omitted.

https://arxiv.org/html/2603.21687v2

[jawilson2]

I don't understand why this is still confusing to people. The second "L" in LLM is language; these things are AWESOME at producing things that SOUND like language, including code. They have so much training data that it is almost always grammatically correct, and often makes sense. Extending this, it has obviously been trained on data containing phrases like "acromial undersurface" and "thin bursal fluid", and "coracromipal arch", in the context of shoulder injury and related imaging. BUT IT DOES NOT KNOW HOW TO DIAGNOSE ANYTHING. So, it SOUNDS like a radiologist or specialist, and might be in the ballpark of correct-ish-ness, but ultimately is a fancy Markov model.

[themantalope]

lol yea I wasn’t going to put in a full dictation on the internet but clearly a lot of misinterpretations from the AI

[deaux]

> Can you share the reasons that you believe this?

Firstly, please keep in mind I'm talking about the entire doctor population of the world here. Not sure which particularly bubble of this earth you have experience with, but note how half the word's population lives in India/China/Indonesia/Pakistan/Nigeria/Brazil/Bangladesh/Russia. Now I do believe that it holds the same for e.g. Europe and non-China East-Asia, but still.

How many patients has the world-wide average doctor seen? How long have they been a doctor?

How many have they seen with the particular condition the patient has?

How much time do they spend listening to and reasoning about a patient? The median in the world is likely under 3 minutes.

How many real-world incentives do human doctors have to deal with?

Given infinite time and resources, and zero external incentives, maybe the median human doctor would outperform the LLM at this task. But this is completely detached from the real world.

> What is special about medical imaging that makes AI/LLMs specifically bad?

LLMs: Besides lack of training data as mentioned elsewhere, they're simply not trained for high-fidelity image processing in general. It's not limited to medical imaging. It's a bit like the "How many Rs in strawberry" thing, but worse.

As for "AI" in general, medical image analysis is a very active field. These tend to be purpose-built though, not general-purpose. It seems likely at some point they'll become mainstream, but there's still a way to go.

[themantalope]

Not to mention shoulder MRs can be hard to read. Findings can be subtle and have to be interpreted in context with the exam, symptoms etc.