Artificial Intelligence (AI) Reveals Ethnic Disparities in Cataract Detection and Treatment.

Introduction: The aim of this work is to identify patients at risk of limited access to healthcare through artificial intelligence using a name-ethnicity classifier (NEC) analyzing the clinical stage of cataract at diagnosis and preoperative visual acuity.

Methods: This retrospective, cross-sectional study includes patients seen in the cataract clinic of a tertiary care hospital between September 2017 and February 2020 with subsequent cataract surgery in at least one eye. We analyzed 4971 patients and 8542 eyes undergoing surgery.

Equal accuracy for Andrew and Abubakar-detecting and mitigating bias in name-ethnicity classification algorithms.

Unlabelled: Uncovering the world's ethnic inequalities is hampered by a lack of ethnicity-annotated datasets. Name-ethnicity classifiers (NECs) can help, as they are able to infer people's ethnicities from their names. However, since the latest generation of NECs rely on machine learning and artificial intelligence (AI), they may suffer from the same racist and sexist biases found in many AIs.