A scalable framework that automatically generates question--answer pairs from longitudinal EHR notes that supports evaluations that static benchmarks cannot, including the generation of multiple answers that reflect variation in clinician reasoning for robust performance assessment and continuously refreshing benchmark...
J. Cahoon, C. Stanwyck, Sulaiman Somani et al.· 1 citation
Large language model (LLM)-based clinical assistants are increasingly being integrated into electronic health record (EHR) systems, transforming how clinicians retrieve and synthesize information from patient records. Their safety and utility depend on rigorous evaluation, yet existing benchmarks are manually curated,...
J. Cahoon, C. Stanwyck, Sulaiman Somani et al.· 0 citations
This work introduces a clinically grounded framework that evaluates leakage along a graded axis of adversarial access, ranging from publicly inferable demographics to leaked note fragments, and provides a practical, reusable framework for contextual privacy evaluation of medical LMs.
Sasha Ronaghi, Sana Tonekaboni, Lena Stempfle et al.· arXiv.org· 0 citations
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