Case-Specific Rubrics for Clinical AI Evaluation: Methodology, Validation, and LLM-Clinician Agreement Across 823 Encounters

This research proposes a methodology for developing and validating case-specific rubrics for evaluating clinical AI systems, particularly focusing on the agreement between Large Language Models (LLMs) and clinicians across a large dataset of patient encounters. This work is crucial for the safe and effective deployment of AI in healthcare, ensuring reliable performance in real-world clinical settings.