Ryan Lail

LLM judges struggle to detect omissions in clinical notes, but a novel task restructuring boosts detection rates significantly.

One in three clinical notes generated by AI scribes contains verified errors, raising critical questions about their reliability in healthcare settings.

Task-specific criteria and ensemble scoring can make smaller LLMs outperform larger ones as judges, unlocking high-accuracy evaluation at a fraction of the cost.