Hasanzadeh Fereshteh, Josephson Colin B, Waters Gabriella, Adedinsewo Demilade, Azizi Zahra, White James A
Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
NPJ Digit Med. 2025 Mar 11;8(1):154. doi: 10.1038/s41746-025-01503-7.
Artificial intelligence (AI) is delivering value across all aspects of clinical practice. However, bias may exacerbate healthcare disparities. This review examines the origins of bias in healthcare AI, strategies for mitigation, and responsibilities of relevant stakeholders towards achieving fair and equitable use. We highlight the importance of systematically identifying bias and engaging relevant mitigation activities throughout the AI model lifecycle, from model conception through to deployment and longitudinal surveillance.
人工智能（AI）正在临床实践的各个方面发挥作用。然而，偏差可能会加剧医疗保健的不平等。本综述探讨了医疗保健人工智能中偏差的根源、缓解策略以及相关利益攸关方在实现公平使用方面的责任。我们强调在人工智能模型的整个生命周期中，从模型构思到部署和长期监测，系统地识别偏差并开展相关缓解活动的重要性。
