Xu Zikang, Li Jun, Yao Qingsong, Li Han, Zhao Mingyue, Zhou S Kevin
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, PR China.
Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, PR China.
NPJ Digit Med. 2024 Oct 17;7(1):286. doi: 10.1038/s41746-024-01276-5.
Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting poorer predictive performance in elderly females. Addressing this fairness issue has become a collaborative effort involving AI scientists and clinicians seeking to understand its origins and develop solutions for mitigation within MedIA. In this survey, we thoroughly examine the current advancements in addressing fairness issues in MedIA, focusing on methodological approaches. We introduce the basics of group fairness and subsequently categorize studies on fair MedIA into fairness evaluation and unfairness mitigation. Detailed methods employed in these studies are presented too. Our survey concludes with a discussion of existing challenges and opportunities in establishing a fair MedIA and healthcare system. By offering this comprehensive review, we aim to foster a shared understanding of fairness among AI researchers and clinicians, enhance the development of unfairness mitigation methods, and contribute to the creation of an equitable MedIA society.
深度学习算法在各种医学图像分析（MedIA）应用中已展现出显著成效。然而，近期研究凸显了这些算法应用于特定亚组时的性能差异，比如在老年女性中表现出较差的预测性能。解决这一公平性问题已成为人工智能科学家和临床医生的一项合作努力，他们试图了解其根源，并在医学图像分析领域开发缓解方案。在本次综述中，我们全面审视了在解决医学图像分析公平性问题方面的当前进展，重点关注方法学途径。我们介绍了群体公平性的基础知识，随后将关于公平医学图像分析的研究分为公平性评估和不公平性缓解两类。还介绍了这些研究中采用的详细方法。我们的综述最后讨论了建立公平的医学图像分析和医疗保健系统中存在的挑战与机遇。通过提供这一全面综述，我们旨在促进人工智能研究人员和临床医生对公平性的共同理解，加强不公平性缓解方法的开发，并为创建一个公平的医学图像分析社会做出贡献。
