Fairer AI in ophthalmology via implicit fairness learning for mitigating sexism and ageism

作者：Weimin Tan, Qiaoling Wei, Zhen Xing, Hao Fu, Hongyu Kong, Yi Lü, Bo Yan, Chen Zhao · 发表于：Nature Communications · 年份：2024 · DOI：10.1038/s41467-024-48972-0 · 被引用次数：14 · 研究领域：Retinal Imaging and Analysis、Retinal and Optic Conditions、Artificial Intelligence in Healthcare and Education

The transformative role of artificial intelligence (AI) in various fields highlights the need for it to be both accurate and fair. Biased medical AI systems pose significant potential risks to achieving fair and equitable healthcare. Here, we show an implicit fairness learning approach to build a fairer ophthalmology AI (called FairerOPTH) that mitigates sex (biological attribute) and age biases in AI diagnosis of eye diseases. Specifically, FairerOPTH incorporates the causal relationship between fundus features and eye diseases, which is relatively independent of sensitive attributes such as race, sex, and age. We demonstrate on a large and diverse collected dataset that FairerOPTH significantly outperforms several state-of-the-art approaches in terms of diagnostic accuracy and fairness for 38 eye diseases in ultra-widefield imaging and 16 eye diseases in narrow-angle imaging. This work demonstrates the significant potential of implicit fairness learning in promoting equitable treatment for patients regardless of their sex or age.