Medicine and Health

Autonomous artificial intelligence for diabetic eye disease increases access and health equity in underserved populations

J. J. Huang, R. Channa, et al.

This groundbreaking research by Jane J. Huang, Roomasa Channa, Risa M. Wolf, Yiwen Dong, Mavis Liang, Jiangxia Wang, Michael D. Abramoff, and T. Y. Alvin Liu reveals how autonomous AI enhances annual diabetic eye disease testing, showing an impressive increase, particularly among underserved Black/African American communities. Discover how this innovation promotes health equity and bridges adherence gaps in testing.

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Abstract

This study investigated the impact of autonomous AI for diabetic eye disease (DED) testing on adherence to annual testing guidelines and health equity in underserved populations. A propensity score weighting analysis comparing AI-switched and non-AI sites within Johns Hopkins Medicine showed a significantly greater increase in DED testing at AI-switched sites (6.7 percentage points). This improvement was particularly pronounced among Black/African Americans (12.2 percentage points increase). Furthermore, the study observed a reduction in adherence rate gaps between different racial and insurance groups, indicating improved health equity.

Publisher

Nature

Published On

Jul 22, 2024

Authors

Jane J. Huang, Roomasa Channa, Risa M. Wolf, Yiwen Dong, Mavis Liang, Jiangxia Wang, Michael D. Abramoff, T. Y. Alvin Liu

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