用智能选例提升多模态医疗诊断公平性，无需调参
Fairness in Multi-modal Medical Diagnosis with Demonstration Selection
- 通过聚类选择语义相关且人口均衡的示例，实现无调参公平推理
- 在多个医学影像基准上显著降低性别、种族、族裔偏差
- 适合追求高效公平的医疗AI研发者和临床部署团队
多模态大语言模型在医学图像推理中展现强大潜力，但跨人口群体的公平性仍是重大挑战。现有去偏方法通常依赖大规模标注数据或微调，对基础模型不实用。本文探索了无需调参的上下文学习（ICL）作为轻量级替代方案。系统分析发现，传统示例选择策略因所选示例存在人口不平衡而无法保障公平性。为此，提出公平感知示例选择（FADS），通过基于聚类的采样构建人口均衡且语义相关的示例。在多个医学影像基准上的实验表明，FADS持续降低了与性别、种族、族裔相关的偏差，同时保持高准确率，为公平医学图像推理提供了一条高效且可扩展的路径。结果凸显了公平感知上下文学习在实现医疗公平中的潜力。
原文摘要 · Abstract (English)
Multimodal large language models (MLLMs) have shown strong potential for medical image reasoning, yet fairness across demographic groups remains a major concern. Existing debiasing methods often rely on large labeled datasets or fine-tuning, which are impractical for foundation-scale models. We explore In-Context Learning (ICL) as a lightweight, tuning-free alternative for improving fairness. Through systematic analysis, we find that conventional demonstration selection (DS) strategies fail to ensure fairness due to demographic imbalance in selected exemplars. To address this, we propose Fairness-Aware Demonstration Selection (FADS), which builds demographically balanced and semantically relevant demonstrations via clustering-based sampling. Experiments on multiple medical imaging benchmarks show that FADS consistently reduces gender-, race-, and ethnicity-related disparities while maintaining strong accuracy, offering an efficient and scalable path toward fair medical image reasoning. These results highlight the potential of fairness-aware in-context learning as a scalable and data-efficient solution for equitable medical image reasoning.
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