Addressing fairness issues in deep learning-based medical image analysis: a systematic review

doi:10.1038/s41746-024-01276-5

	Addressing fairness issues in deep learning-based medical image analysis: a systematic review
	Xu, Zikang 1,2; Li, Jun 3; Yao, Qingsong 3; Li, Han 1,2; Zhao, Mingyue 1,2; Zhou, S. Kevin 1,2,3,4
	2024-10-17
发表期刊	NPJ DIGITAL MEDICINE
ISSN	2398-6352
卷号	7 期号:1 页码:16
摘要	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.
DOI	10.1038/s41746-024-01276-5
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (National Science Foundation of China)[62271465] ; Natural Science Foundation of China[SYG202338] ; Suzhou Basic Research Program[YKY-KF202206] ; Open Fund Project of Guangdong Academy of Medical Sciences, China
WOS研究方向	Health Care Sciences & Services ; Medical Informatics
WOS类目	Health Care Sciences & Services ; Medical Informatics
WOS记录号	WOS:001335039900001
出版者	NATURE PORTFOLIO
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39492
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhou, S. Kevin
作者单位	1.Univ Sci & Technol China, Sch Biomed Engn, Div Life Sci & Med, Hefei, Anhui, Peoples R China 2.Univ Sci & Technol China, Suzhou Inst Adv Res, Ctr Med Imaging Robot Analyt Comp & Learning MIRAC, Suzhou, Jiangsu, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, CAS, Beijing, Peoples R China 4.Univ Sci & Technol China, Key Lab Precis & Intelligent Chem, Hefei, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Xu, Zikang,Li, Jun,Yao, Qingsong,et al. Addressing fairness issues in deep learning-based medical image analysis: a systematic review[J]. NPJ DIGITAL MEDICINE,2024,7(1):16.
APA	Xu, Zikang,Li, Jun,Yao, Qingsong,Li, Han,Zhao, Mingyue,&Zhou, S. Kevin.(2024).Addressing fairness issues in deep learning-based medical image analysis: a systematic review.NPJ DIGITAL MEDICINE,7(1),16.
MLA	Xu, Zikang,et al."Addressing fairness issues in deep learning-based medical image analysis: a systematic review".NPJ DIGITAL MEDICINE 7.1(2024):16.