Healthcare Benchmarks Are Only as Good as Their Assumptions (blog.ml.cmu.edu)

Recent research by Bean et al. (2025) highlights a significant evaluation-deployment gap in healthcare settings where large language models (LLMs) are used as medical assistants, showing a 61 percentage point drop in performance from evaluation to real-world deployment. The study argues that this gap is not due to flawed benchmarks but arises from implicit assumptions in evaluation protocols that fail to hold true in practice. To address this, the researchers propose a new framework, BenchmarkCards, which categorizes assumptions into task and outcome types, enabling practitioners to identify discrepancies between benchmark results and actual deployment contexts. The implications of this research are profound for the AI/ML community, particularly in healthcare. It emphasizes the need for benchmarks to explicitly document their assumptions about user interactions and the intended outcomes, ensuring that the conditions used for evaluating LLMs align with real-world scenarios. The proposed staged evaluation process enhances the ability to test these assumptions progressively, encouraging a more robust understanding of model performance that extends beyond mere accuracy to include human behavior and decision-making. Ultimately, this framework aims to foster safer and more effective deployment of LLMs in clinical settings by guiding the development of benchmarks that are contextually relevant.