Healthcare benchmarks are only as good as their assumptions

Summary

In healthcare settings, LLM performance differs between evaluation and deployment, with a recent study finding a 61 percentage point gap. The authors argue this gap arises not from poorly designed benchmarks but from implicit assumptions in evaluation protocols that fail at deployment. They propose a taxonomy categorizing assumptions into task and outcome types to diagnose where the gap arises and what is needed to close it. Closing the gap requires making assumptions explicit, testing them, and updating protocols accordingly. The findings highlight the need for evaluation paradigms that align with real-world deployment.