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The Impact of Race, Ethnicity, and Sex on Fairness in Artificial Intelligence for Glaucoma Prediction Models

Objective: Despite advances in artificial intelligence (AI) in glaucoma prediction, most works lack multicenter focus and do not consider fairness concerning sex, race, or ethnicity. This study aims to examine the impact of these sensitive attributes on developing fair AI models that predict glaucom...

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Hlavní autoři: Rohith Ravindranath, MS, Joshua D. Stein, MD, MS, Tina Hernandez-Boussard, A. Caroline Fisher, Sophia Y. Wang, MD, MS, Sejal Amin, Paul A. Edwards, Divya Srikumaran, Fasika Woreta, Jeffrey S. Schultz, Anurag Shrivastava, Baseer Ahmad, Paul Bryar, Dustin French, Brian L. Vanderbeek, Suzann Pershing, Anne M. Lynch, Jennifer L. Patnaik, Saleha Munir, Wuqaas Munir, Joshua Stein, Lindsey DeLott, Brian C. Stagg, Barbara Wirostko, Brian McMillian, Arsham Sheybani, Soshian Sarrapour, Kristen Nwanyanwu, Michael Deiner, Catherine Sun, Houston: Robert Feldman, Rajeev Ramachandran
Médium: Artigo
Jazyk:Inglês
Vydáno: Elsevier 2025-01-01
Edice:Ophthalmology Science
Témata:
Bias
Fairness
Glaucoma
Health disparities
Machine learning
On-line přístup:http://www.sciencedirect.com/science/article/pii/S2666914524001325
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