提出医疗影像公平性框架，系统解决基础模型偏见问题。
Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives
- 从数据到部署全链条设计公平性干预策略
- 强调需结合技术与政策手段应对系统性偏见
- 适合关注医疗AI伦理与公平性的研究者
医疗人工智能的公平性要求系统在所有人口群体中做出无偏决策，融合技术创新与伦理原则。基础模型（FMs）通过自监督学习在大规模数据上训练，可在医疗影像任务中高效迁移，减少对标注数据的依赖。尽管其具备提升公平性的潜力，但在不同人口群体间保持一致性能仍面临重大挑战。本综述指出，有效缓解模型偏见需贯穿开发全过程的系统性干预。以往方法多聚焦模型层面，而我们的分析表明，实现基础模型公平性需要整合数据记录、模型训练、评估与部署协议等全流程干预。该综合框架通过展示系统性偏见缓解与政策协同如何应对技术和制度障碍，推进了当前认知。构建公平的基础模型是实现先进医疗技术普惠化的重要一步，尤其对资源匮乏地区和弱势人群意义重大。
原文摘要 · Abstract (English)
Ensuring equitable Artificial Intelligence (AI) in healthcare demands systems that make unbiased decisions across all demographic groups, bridging technical innovation with ethical principles. Foundation Models (FMs), trained on vast datasets through self-supervised learning, enable efficient adaptation across medical imaging tasks while reducing dependency on labeled data. These models demonstrate potential for enhancing fairness, though significant challenges remain in achieving consistent performance across demographic groups. Our review indicates that effective bias mitigation in FMs requires systematic interventions throughout all stages of development. While previous approaches focused primarily on model-level bias mitigation, our analysis reveals that fairness in FMs requires integrated interventions throughout the development pipeline, from data documentation to deployment protocols. This comprehensive framework advances current knowledge by demonstrating how systematic bias mitigation, combined with policy engagement, can effectively address both technical and institutional barriers to equitable AI in healthcare. The development of equitable FMs represents a critical step toward democratizing advanced healthcare technologies, particularly for underserved populations and regions with limited medical infrastructure and computational resources.
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