# Leveraging Artificial Intelligence to Prevent Vision Loss from Diabetes Among Socioeconomically Disadvantaged Communities

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $878,774

## Abstract

PROJECT SUMMARY
Diabetes mellitus affects 37 million US adults and is the leading cause of vision loss among adults aged 18-64
years. Countries such as the UK that have robust eye screening and treatment programs successfully prevent
blindness from diabetes. In the US, where screening programs have been less successful, usual-care screening
involves a primary care provider referring patients with diabetes to an eye care provider for a dilated eye exam.
Barriers to usual-care screening include transportation, cost, and the time required for patients to make and
attend this separate eye care appointment. Racial/ethnic minorities and socioeconomically disadvantaged
communities, such as individuals on Medicaid, are more affected by these challenges, resulting in lower
screening rates and higher rates of preventable vision loss. Thus, an urgent need exists for a program that
equitably improves eye screening and follow-up eye care rates for patients with diabetes. The overall objective
of this proposal is to investigate an FDA-approved artificial intelligence (AI)-based system that allows primary
care providers to identify diabetic eye disease at the primary care clinic without the need for oversight by an eye
care provider. The novel intervention we are testing, AI-BRIDGE (Artificial Intelligence-Based point of caRe,
Incorporating Diagnosis, SchedulinG, and Education), is an autonomous AI-based protocol that provides
screening for diabetic eye disease at primary care visits, as well as culturally adapted patient education on
diabetic eye disease and, if a patient screens positive, assistance with scheduling an in-person, follow-up eye
care visit. To achieve our objective, we will carry out 2 specific aims: (1) Determine whether, relative to usual-
care screening, AI-BRIDGE improves eye screening and follow-up care rates across races/ethnicities and
reduces racial/ethnic disparities in screening rates. To do so, we will work with stakeholders to adapt AI-BRIDGE
to an underserved primary care setting and then conduct a stepped-wedge, cluster randomized controlled trial
of the adapted intervention in partnership with 10 clinics that are Federally Qualified Healthcare Centers,
providing primary care to medically underserved communities. (2) Identify determinants of, and strategies to
promote, effective and equitable implementation of AI-BRIDGE using a mixed methods approach. We
hypothesize that factors such as organizational leadership’s commitment to the intervention, competing demands
on the clinic, and patient and provider perceptions of AI will contribute to adoption of AI-BRIDGE. To test this
hypothesis, we will conduct semi-structured interviews of patients, clinic leadership, and providers to identify
barriers and facilitators, and then work with stakeholders to identify strategies to address the barriers identified.
This work is innovative because it is the first-ever randomized controlled trial that (1) evaluates whether AI can
improve equity in ...

## Key facts

- **NIH application ID:** 10854453
- **Project number:** 1R01EY035994-01
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Roomasa Channa
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $878,774
- **Award type:** 1
- **Project period:** 2024-08-01 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10854453

## Citation

> US National Institutes of Health, RePORTER application 10854453, Leveraging Artificial Intelligence to Prevent Vision Loss from Diabetes Among Socioeconomically Disadvantaged Communities (1R01EY035994-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10854453. Licensed CC0.

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