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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Detalles Bibliográficos

Principais autores:	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
Formato:	Artigo
Idioma:	Inglês
Publicado:	Elsevier 2025-01-01
Series:	Ophthalmology Science
Assuntos:	Bias Fairness Glaucoma Health disparities Machine learning
Acceso en liña:	http://www.sciencedirect.com/science/article/pii/S2666914524001325
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