That does make sense but the order of it might be: doctor analysis. Then show LLM as double check to doctor. Doctor assesses and may keep en improve the doctors first analysis?
Another problem is that general models' performance just sucks. From an upcoming conf. talk (in pathology) where they ran 2 Medgemma models on 100 slides with known diagnosis:
> Results: Full concordance with the reference diagnosis was 8% (27B) and 5% (1.5 4B; McNemar p=0.68), while partial matches were 29% vs 20% respectively (McNemar p=0.053). When correct diagnoses anywhere in the differential were counted, 51% (27B) vs 30% (1.5 4B), with 27B significantly superior (McNemar χ²=12.1, p=0.0005). Site-level performance varied widely (30–100%). Both models reported HIGH confidence in ~99% of cases irrespective of correctness.
i.e. highly confident, wrong 95% of time. in 49% of cases the real diagnosis wasn't even on models' differential. Doctor can hardly improve using something they can safely assume to be just noise.
Totally agree on that, if you have to look constantly at something 95% there is no value. The expectation of course is that it will be better. But if not at a certain level useless.
Not sure how that research compares to the claims being made by many that a second opinion via ai in the end led to changes in treatment. Likely people spent quite some time searching and figuring out. That would be a different and n=1 result. Don't have enough knowledge of that research to determine how much result can be gained when the models are managed in a way that produces better results.
And of course how much time/effort/cost that would take. How much is custom and how much is an automated programmable flow.
> Results: Full concordance with the reference diagnosis was 8% (27B) and 5% (1.5 4B; McNemar p=0.68), while partial matches were 29% vs 20% respectively (McNemar p=0.053). When correct diagnoses anywhere in the differential were counted, 51% (27B) vs 30% (1.5 4B), with 27B significantly superior (McNemar χ²=12.1, p=0.0005). Site-level performance varied widely (30–100%). Both models reported HIGH confidence in ~99% of cases irrespective of correctness.
i.e. highly confident, wrong 95% of time. in 49% of cases the real diagnosis wasn't even on models' differential. Doctor can hardly improve using something they can safely assume to be just noise.
https://ecp2026.abstractserver.com/programme/#/scientific/de...