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Across this controlled multilingual corpus, aggregate transcription-frequency metrics did not reliably track the sparse severe tail of clinically consequential errors, and these data do not support its use alone as a proxy for clinical safety.
A pipeline that converts free-text reports into multi-label matrices and then reconfigures the label schema through dictionary edits rather than new inference passes, i.e., without relabeling the corpus, suggests a different unit of work for radiology labeling.
The judges behave as a consistent, near-non-differential, clinician-equivalent instrument, which licenses a directional AI-versus-clinician contrast under a non-differential misclassification argument, subject to its conditions.
In this simulation, AI-generated notes scored higher on documentation quality, varied less, and carried fewer clinically significant errors than notes written on the same consultations by junior-to-middle-grade clinicians.
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