Healthcare LLM Benchmarks Are Only as Good as Their Explicit Assumptions
Benchmarks are necessary for healthcare evaluation, but are not sufficient for predicting deployment performance. Our position is that the evaluation--deployment gap arises not because of poorly designed benchmarks, but from implicit assumptions about how users interact with models that cannot be surfaced from benchmarks alone. To make this precise, we propose a classification of assumptions into two categories: task, which can be tested from conversation data alone, and outcome, which requires outcome data and behavioral studies for testing. Critically, outcome assumptions depend on human behavior, something that even well-designed benchmarks cannot directly observe. To demonstrate the o...
| Authors | Naveen Raman, Santiago Cortes-Gomez, Mateo Dulce Rubio et al. |
|---|---|
| Primary category | cs.CY |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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