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.

翻译：确保医疗领域人工智能的公平性，要求系统能够在所有人口统计群体中做出无偏决策，从而将技术创新与伦理原则相融合。基础模型通过自监督学习在庞大数据集上训练而成，能够在减少对标注数据依赖的同时，高效适应各类医学影像任务。这些模型展现出提升公平性的潜力，但在实现跨人口统计群体的稳定性能方面仍面临重大挑战。我们的综述表明，在基础模型中实现有效的偏见缓解需要在开发全阶段进行系统性干预。尽管先前方法主要关注模型层面的偏见缓解，但我们的分析揭示，基础模型的公平性需要在从数据记录到部署协议的全开发流程中进行整合干预。这一综合框架通过展示系统性偏见缓解与政策参与相结合，如何有效应对医疗公平人工智能的技术与制度障碍，从而推进了当前认知。公平基础模型的发展，是向普及先进医疗技术迈出的关键一步，尤其对于医疗服务不足的人群及医疗基础设施与计算资源有限的地区具有重要意义。