Machine Learning (Article)

Large language models (LLMs) are designed to generate answers to user prompts, which often drives them to respond even when uncertainty is high, information is incomplete, or a refusal would be more appropriate. In healthcare, this tendency can be dangerous: confidently stated but inaccurate medical advice can cause significant harm, making the ability to abstain especially important. In this paper, we review studies investigating LLM abstention behaviors in healthcare. The literature highlights two main motivations: (1) uncertainty-driven abstention, where the model withholds a response when confidence is low, and (2) safety-driven abstention, where the model declines to provide potentially harmful information. Most existing mechanisms are extrinsic and rely on auxiliary tools to determine when to abstain. We find that state-of-the-art LLMs still struggle to refuse inappropriate prompts, and that few benchmarks evaluate abstention in realistic medical scenarios, where performance lags behind other domains. Building on these findings, we introduce a decision-theoretic formalization of abstention that models the trade-off between answering and withholding responses under uncertainty and potential harm. Based on this formulation, we present MedSAFE, a framework for evaluating abstention in clinical dialogs, and demonstrate its operationalization through a proof-of-concept pilot across clinical scenarios derived from the review.