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

ABSTRACT:

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 glaucoma progression to necessitating incisional glaucoma surgery.

Design

Database study.

Participants

Thirty-nine thousand ninety patients with glaucoma, as identified by International Classification of Disease codes from 7 academic eye centers participating in the Sight OUtcomes Research Collaborative.

Methods

We developed XGBoost models using 3 approaches: (1) excluding sensitive attributes as input features, (2) including them explicitly as input features, and (3) traini

SUBMITTER: Ravindranath R 

PROVIDER: S-EPMC11462200 | biostudies-literature | 2025 Jan-Feb

REPOSITORIES: biostudies-literature