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This work demonstrates it can estimate where a judge is ignorant rather than where experts genuinely disagree, and proposes using this to direct expert labelling.
Audited three commercial AI scribes on the same 142 consultations: 565 notes from recorded UK primary-care and US ambulatory encounters plus authored scenarios, with one failure mode drawn from published scribe-error taxonomies.
It is asked whether judges detect omissions in clinical notes, and two methods reach it independently and trade off: a per-fact pipeline, and a GEPA-evolved prompt doing the same in one call.
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