I agree with you on this specific study, however, I can't really wrap my head about the fact that doctors will be better than AI models on the long-run. After all, medicine is all about knowledge, experience and intelligence (maybe "pattern recognition"), all those, we must assume that the best AI models (especially ones focusing solely in the medical field) would largely beat large majority of humans (aka doctors), if we already have this assumption for software engineers, we should have it for this field as well, and let's be realistic, each time I've seen a doc the last few months (and ER twice), each time they were using ChatGPT btw (not kidding, it chocked me).

So I’m genuinely curious:

What is the specific capability (or combination of capabilities) that people believe will remain permanently (or at least for decades) where a top medical AI cannot match or exceed the performance of a good human doctor? Let's put liability and ethics aside, let's be purely objective about it.

> very hesitant to trust studies like this

Why? Simply because there is a plethora of "studies" from the AI industry benchmaxing? Or that every single time the outcome is in favor of the tools then when actually checking the methodology they are comparing apple and oranges? Truly I don't get your skepticism. /s obviously.

Jokes aside whenever I read about such a study from a field that is NOT mine I try to get the opinion of an actual expert. They actually know the realistic context that typically make the study crumble under proper scrutiny.

Yup, there's a reason while ROC is a thing in data science. You can build a 99% accurate cancer detector that's just a slip of paper saying 'you don't have cancer', but everybody understands its worthless intuitively. With more complex setups, that intuition goes away.

Ultimatly you'd want humans and AI to study separately cases separately and independtly, and flag cases that have been found by only one analysis so that a separate analysis is done by a second pair of eyes.

When you read through the article it shows that the gap between doctors and LLMs actually disappeared (in terms of statistical significance) once both were allowed to read the full case notes.

The headline is quoting a number based on guessed diagnoses from nurse's notes. The LLM was happier to take guesses from the selected case studies than the doctors is my guess.

In a study like this, there’s also a difference in motivation. An AI will mechanically “take the study seriously.” I’m not convinced the doctors will.

But when making decisions about a real patient’s care, a doctor will be operating under different motivations.

They can also refer patients to a specialist, defer a diagnosis until they have more information, use external resources, consult with other doctors.

Doctors aren’t chatbots. They are clinical care directors.

Presuming there are no issues with information leakage, it’s genuinely impressive AI can perform this level of success at a specific doctoring skill. That doesn’t make it a replacement for a doctor. It does make it a useful tool for a doctor or a patient, which is exactly what we’re seeing in practice.

Interestingly, this recent study using ChatGPT Health gave quite a different outcome (https://www.nature.com/articles/s41591-026-04297-7). Here it was wrong about emergency triage 50% of the time.

> the human doctors don't just look at the notes to diagnose the ER patient

From my limited experience hanging on ER hallways for other people, they don't look at the notes, they look at the damn patient.

I think AI can be useful in any kind of context interpretation, but not make a decision.

Could be running in the background on patient data and message the doctor "I see X in the diagnostic, have you ruled out Y, as it fits for reasons a, b, c?"

I like my coding agents the same way, inform me during review on things that I've missed. Instead of having me comb through what it generates on a first pass.

hallucination on steroids, wow. I had to read through the abstract to believe it:

"In the most extreme case, our model achieved the top rank on a standard chest Xray question-answering benchmark without access to any images."

I think it's plausible since doctors tend to have human cognitive biases and miss things. People tend to fixate on patterns they're most familiar with.

I haven't finished reading the linked paper, but I'm intrigued by the assumption that the results show illusion or mirage results when not giving access to the x-rays.

It seems like a very reasonable take away, but it skips the other one. Do x-rays make results less accurate?

These type of experiments are bound to have biases depending on who is doing it and who is funding it. The experiment is being funded for a particular reason itself to move the narrative in a desired direction. This is probably a good reason to have government funded research in these type of sensitive areas.

Weird that this is the case and a new study.

but those kind of x-ray models are already activly used. They are not used though as a only and final diagnosis. Its more like peer review and priorization like check this image first because it seems most critical today.

I'm even more concerned that current models are not trained to say no, or to even recognize most failure modes.

"Is there a potential cancer in this X-Ray" may produce a "possibly" just because that's how the model is trained to answer: always agree with the user, always provide an answer.

Oh, and don't forget that "Is there a potential cancer in this X-Ray" and "Are there any potential problems in this X-Ray" are two completely different prompts that will lead to wildly different answers.

I think the bigger takeaway here is that 50% of the time doctors will miss what you have.