Evaluating the impact of social determinants of health on model performance and fairness in artificial intelligence for glaucoma progression prediction.

This is the authors' abstract. We don't add key points for this paper.

OBJECTIVE: To evaluate the impact of incorporating community-level social determinants of health (SDoH) on the performance and algorithmic fairness of artificial intelligence (AI) models trained to predict glaucoma progression to surgery. MATERIALS AND METHODS: This multicenter study included 63 268 glaucoma patients across 12 centers. Using electronic health records and community-level SDoH features (Distressed Communities Index [DCI] and rural-urban commuting area [RUCA] codes), we developed XGBoost models to predict progression to incisional glaucoma surgery. We compared SDoH-unaware and SDoH-aware models across an internal test set and 3 diverse external test sets, evaluating area under the receiver operating characteristic curve (AUROC) (performance) and generalized equalized odds (GEO) (fairness). RESULTS: Including SDoH features resulted in negligible changes in predictive performance across the internal and all external test sets. However, the SDoH-aware model improved algorithmic fairness (achieving lower GEO scores) in the internal set (0.098 vs 0.216) and a mixed-population external site (0.172 vs 0.378). Conversely, inclusion of SDoH marginally improved at a highly urbanized, low-DCI external site (0.491 vs 0.504) and failed to improve at a rural, high-DCI external site (0.241 vs 0.228). DISCUSSION: Incorporating SDoH features can improve algorithmic fairness regarding socioeconomic disparities without sacrificing predictive accuracy; however, these benefits do not universally transfer to external sites with demographically dissimilar cohorts. CONCLUSION: Incorporating community-level SDoH features into glaucoma prediction models improved algorithmic fairness with respect to socioeconomic disparities, without significantly altering predictive accuracy. However, fairness benefits did not universally generalize to demographically dissimilar external cohorts. AI models require rigorous external validation across diverse populations to ensure equity prior to clinical implementation.