Case-specific clinician rubrics for clinical AI notes achieve strong discrimination between outputs, high stability, and clinician-LLM agreement matching clinician-clinician levels at far lower cost.
Monitoring performance of clinical artificial intelligence in health care: a scoping review
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A multi-channel governance framework for a deployed ambient AI scribe achieved measurable improvements in clinician-validated performance and feedback quality through continuous rubric evaluation, live monitoring, and controlled experiments.
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Case-Specific Rubrics for Clinical AI Evaluation: Methodology, Validation, and LLM-Clinician Agreement Across 823 Encounters
Case-specific clinician rubrics for clinical AI notes achieve strong discrimination between outputs, high stability, and clinician-LLM agreement matching clinician-clinician levels at far lower cost.
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End-to-End Evaluation and Governance of an EHR-Embedded AI Agent for Clinicians
A multi-channel governance framework for a deployed ambient AI scribe achieved measurable improvements in clinician-validated performance and feedback quality through continuous rubric evaluation, live monitoring, and controlled experiments.