Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease
Document Type
Article
Publication Date
12-19-2024
Publication Title
npj Digital Medicine
Volume
7
Pages
1-10
Publisher Name
Springer Nature
Abstract
Where adopted, Autonomous artificial Intelligence (AI) for Diabetic Retinal Disease (DRD) resolves longstanding racial, ethnic, and socioeconomic disparities, but AI adoption bias persists. This preregistered trial determined sensitivity and specificity of a previously FDA authorized AI, improved to compensate for lower contrast and smaller imaged area of a widely adopted, lower cost, handheld fundus camera (RetinaVue700, Baxter Healthcare, Deerfield, IL) to identify DRD in participants with diabetes without known DRD, in primary care. In 626 participants (1252 eyes) 50.8% male, 45.7% Hispanic, 17.3% Black, DRD prevalence was 29.0%, all prespecified non-inferiority endpoints were met and no racial, ethnic or sex bias was identified, against a Wisconsin Reading Center level I prognostic standard using widefield stereoscopic photography and macular Optical Coherence Tomography. Results suggest this improved autonomous AI system can mitigate AI adoption bias, while preserving safety and efficacy, potentially contributing to rapid scaling of health access equity.
Recommended Citation
Abramoff, Michael D.; Lavin, Philip T.; Jakubowski, Julie R.; Blodi, Barbara A.; Keeys, Mia; Joyce, Cara; and Folk, James C., "Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease" (2024). Biostatistics Collaborative Core: Faculty Publications and Other Works. 21.
https://ecommons.luc.edu/biostatistics_facpubs/21
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Statement
© The Author(s), 2024.
Comments
Author Posting © The Author(s), 2024. This article is posted here by permission of Springer Nature for personal use and redistribution. This article was published open access in npj Digital Medicine, Vol. 7 (2024), https://doi.org/10.1038/s41746-024-01389-x.