Biomarker-based AI linked to racial bias risk in retinal images

JAMA Network

About The Study: Results of this diagnostic study including 4,095 retinal fundus images collected from 245 neonates suggest that it can be very challenging to remove information relevant to self-reported race from fundus photographs. As a result, AI algorithms trained on fundus photographs have the potential for biased performance in practice, even if based on biomarkers rather than raw images. Regardless of the methodology used for training AI, evaluating performance in relevant subpopulations is critical.

Authors: J. Peter Campbell, M.D., M.P.H., of the Oregon Health & Science University in Portland, is the corresponding author.

To access the embargoed study: Visit our For The Media website at this link https://media.jamanetwork.com/

(doi:10.1001/jamaophthalmol.2023.1310)

/Public Release. This material from the originating organization/author(s) might be of the point-in-time nature, and edited for clarity, style and length. Mirage.News does not take institutional positions or sides, and all views, positions, and conclusions expressed herein are solely those of the author(s).View in full here.