医疗AI模型在不同患者群体中表现差异大，需细分评估才可安全使用。
One Size Fits None: Rethinking Fairness in Medical AI
- 按患者特征分组评估模型性能，发现整体好不代表各群组都好
- 真实医疗数据噪声多、不平衡，导致部分群体预测准确率低
- 适合关注医疗AI公平性与临床部署风险的研究者阅读
机器学习模型正日益用于辅助临床决策。然而，现实医疗数据常存在噪声、缺失和不平衡问题，导致模型在不同患者子群体中表现不一。这种差异引发公平性担忧，尤其可能加剧弱势群体的不利处境。本文分析多个医疗预测任务，揭示模型性能随患者特征变化的情况。尽管模型整体表现良好，我们强调必须进行子群体层面的评估，才能识别性能差距——这既有助于临床实践中审慎对待模型结果，也能指导更负责任的模型开发。本研究推动了对医疗机器学习模型在子群体敏感性上的发展与部署的实践讨论，强调公平性与透明性的紧密关联。
原文摘要 · Abstract (English)
Machine learning (ML) models are increasingly used to support clinical decision-making. However, real-world medical datasets are often noisy, incomplete, and imbalanced, leading to performance disparities across patient subgroups. These differences raise fairness concerns, particularly when they reinforce existing disadvantages for marginalized groups. In this work, we analyze several medical prediction tasks and demonstrate how model performance varies with patient characteristics. While ML models may demonstrate good overall performance, we argue that subgroup-level evaluation is essential before integrating them into clinical workflows. By conducting a performance analysis at the subgroup level, differences can be clearly identified-allowing, on the one hand, for performance disparities to be considered in clinical practice, and on the other hand, for these insights to inform the responsible development of more effective models. Thereby, our work contributes to a practical discussion around the subgroup-sensitive development and deployment of medical ML models and the interconnectedness of fairness and transparency.
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