With the development of information technology, artificial intelligence shows great potentials for clinical diagnosis and treatment. Nevertheless, bias in algorithms derived by artificial intelligence can lead to problems such as unequal distribution of healthcare resources, which significantly affect patients' health equity. Algorithmic bias is a technical manifestation of human bias, whose formation strongly correlates with the entire development process of artificial intelligence, starting from data collection, model training and optimization to output application. Healthcare providers, as the key direct participants in ensuring patients' health, should take corresponding measures to prevent algorithmic bias to avoid its related health equity issues. It is important for healthcare providers to ensure the authenticity and unbiasedness of health data, optimize the fairness of artificial intelligence, and enhance the transparency of its output application. In addition, healthcare providers need to consider how to tackle bias-related health inequity, so as to comprehensively ensure patients' health equity. In this study, we reviewed the causes and coping strategies related to algorithmic bias in healthcare, with the aim of improving healthcare providers' awareness and ability to identify and address algorithmic bias, and laying a foundation for ensuring patients' health equity in the information age.

随着信息技术的发展，人工智能为疾病诊疗带来重要价值。然而，人工智能中存在算法偏见现象，可导致医疗卫生资源分配不均等问题，严重损害患者的健康公平。算法偏见是人为偏见的技术化体现，其形成与人工智能开发过程密切相关，主要源于数据收集、训练优化和输出应用3个方面。医护工作者作为患者健康的直接参与者，应采取相应措施以预防算法偏见，避免其引发健康公平问题。医护工作者需保障健康数据真实无偏见、优化人工智能的公平性和加强其输出应用的透明度，同时需思考如何处理临床实践中算法偏见引发的不公平现象，全面保障患者健康公平。本研究就健康领域中算法偏见的形成原因和应对策略展开综述，以期提高医护工作者识别和处理算法偏见的意识与能力，为保障信息化时代中的患者健康公平提供参考。