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

Weimin Tan, Qiaoling Wei, Zhen Xing, Hao Fu, Hongyu Kong, Yi Lu (), Bo Yan () and Chen Zhao ()
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Weimin Tan: Fudan University
Qiaoling Wei: Fudan University
Zhen Xing: Fudan University
Hao Fu: Fudan University
Hongyu Kong: Fudan University
Yi Lu: Fudan University
Bo Yan: Fudan University
Chen Zhao: Fudan University

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41467-024-48972-0

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