MAPPE: Rethinking and Improving Fairness in LLMs for Medicine via Minimax Preference-based Prompt Evolution
Large Language Models (LLMs) have shown strong potential in medical applications such as question answering and clinical prediction. % Despite their growing adoption, fairness in LLMs for medicine remains underexplored, largely due to the mismatch between conventional fairness constraints and the clinically meaningful role of sensitive attributes. Existing approaches often enforce attribute-invariant constraints, leading to substantial performance degradation that is unacceptable in high-stakes healthcare settings. Moreover, fairness evaluation for medical LLMs is hindered by the lack of dedicated benchmarks. In this paper, we first rethink fairness in LLMs for medicine from a clinically grounded, utility-based perspective. Inspired by principles of health equity in medicine, we introduce universal fairness, a clinically grounded definition that reframes fairness as maximizing subgroup-aware diagnostic performance under attribute-conditioned health disparities. To achieve this objective in practice, we propose MAPPE, a training-free minimax prompt optimization framework. % MAPPE theoretically promotes universal fairness, while directly applicable to both closed-source and open-source LLMs. To systematically evaluate fairness, we construct FairMed, the first attribute-annotated benchmark for medical LLMs covering medical question answering and clinical prediction. % Experiments on both closed-source and open-source LLMs reveal demographic disparities, while MAPPE consistently improves worst-group and overall performance, outperforming existing fairness-oriented and prompt-based methods. The dataset and code are available at https://github.com/xiye7lai/FairMed.