The more training data, the more questions it can answer with a reasonable degree of probability of accuracy.
Throwing away a potentially useful analysis just because it’s probabilistic seems a bit like throwing the baby out with the bath water.
This case is about handing a 3D imaging result to a text predictor and hoping for a valid second opinion.
The real question is where’s the cut-off point between accuracy and utility.
Remember: a second human opinion can also be wrong, and even a wrong opinion can still be useful (especially in medicine where differential diagnoses are a common practice - if the LLM gives you a useless opinion, you rule it out and move on).
I don’t think it’s particularly unreasonable to think that an LLM would have enough literature, or enough reasoning ability, to be able to generate a plausible interpretation of the data. A human can then review and say either “yeah that’s clearly not the case here” or “hmm, actually that could explain it, maybe we should order another test”.