In the current development and deployment of many artificial intelligence
(AI) systems in healthcare, algorithm fairness is a challenging problem in
delivering equitable care. Recent evaluation of AI models stratified across
race sub-populations have revealed inequalities in how patients are diagnosed,
given treatments, and billed for healthcare costs. In this perspective article,
we summarize the intersectional field of fairness in machine learning through
the context of current issues in healthcare, outline how algorithmic biases
(e.g. - image acquisition, genetic variation, intra-observer labeling
variability) arise in current clinical workflows and their resulting healthcare
disparities. Lastly, we also review emerging technology for mitigating bias via
federated learning, disentanglement, and model explainability, and their role
in AI-SaMD development.

这篇综述论文概述了高科技医疗领域中人工智能系统的算法公正性问题，阐述了当前临床工作流程中算法偏见（如图像采集，基因变异，内观者标记变异性等）导致的不公平医疗差距并探讨了联邦学习、分离和模型解释等新兴技术在 AI-SaMD 发展中的作用。