Why AI Won't Fully Replace Radiologists: Three Structural Barriers, Explained
gregmushen · x · 2026-09-26
AI practitioner gregmushen explains why AI struggles to fully replace radiologists, drawing an analogy to autonomous driving safety.
- His reading: experienced radiologists realize after 10k X-rays how difficult and nuanced the work is — which is exactly why AI can't fully replace it.
- He lists three structural disadvantages that make training radiology models hard.
- FSD analogy: in self-driving, crashes happen but you usually don't know the model 'crashed,' and the reason almost never feeds back into the model. Radiology is nearly the opposite — making safety hard even with clean data, with an extreme feedback-loop problem.
Related event: Radiologists push back: AI replacing them is far harder than VCs think(4 posts)→
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