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R. Sanghera

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#small language model Open access Sep 2026

Beyond word error rate: clinical risk as the necessary standard for ambient AI scribe evaluation: evidence from 77 global languages

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.

H. Bergman, V. Liu, B. Austin et al. · 0 citations
Review Open access Aug 2026

Are automated documentation-error judges fit to measure ambient AI scribes? A pre-registered, blinded human-validation study

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.

H. Bergman, V. Liu, B. Austin et al. · 1 citation · ⚡1

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