Toward Better Assessment of LLMs' Performance in Clinical Error Detection
Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation. Error-detection benchmarks are typically constructed by injecting errors into notes, such that each erroneous note has a natural counterpart. Aggregate discriminative metrics (e.g., balanced accuracy or F1) do not exploit this structure. We show that this omission is consequential. In particular, evaluating 15 diverse LLMs on 4 standardized clinical error-detection test sets acro
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- PossiblePossibly related (embedding) · 52%Benchmarking large language models against practicing clinicians on psychopathological assessment - Nature →
- PossiblePossibly related (embedding) · 51%Addressing benchmarking gaps in large language models for health and medicine with dynamic red-teaming - Nature →
- LinkedLinked via arxiv author · 85%Yifan Zhang →
“Toward Better Assessment of LLMs' Performance in Clinical Error Detection”
- LinkedLinked via arxiv author · 85%Rahmatollah Beheshti →
“Toward Better Assessment of LLMs' Performance in Clinical Error Detection”