# Autonomous AI to mitigate disparities for diabetic retinopathy screening in youth during and after COVID-19

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $343,488

## Abstract

Project Summary
In 2018 the FDA approved the first autonomous healthcare artificial intelligence (AI-HC) software that interprets
retinal images taken with a non-mydriatic fundus camera, providing an immediate result for diabetic retinopathy
(DR) screening at the point of care (POC) for adults with diabetes. The PIs of the parent award were the first to
implement this technology in pediatrics, demonstrating safety, effectiveness and equity, and cost-savings to
the patient. They also found that minority youth, those with lower household income and Medicaid insurance
were less likely to undergo recommended screening, yet were more likely to have DR. The parent award
hypothesizes that implementing POC autonomous AI in the diabetes care setting will increase DR screening
rates in youth with diabetes, mitigate disparities in access to screening, and be cost-effective to the health care
system. In the parent award, Aim 1 is a randomized control trial at two clinic sites to determine if autonomous
AI increases screening compared to an eye-care professional (ECP), and if those who screen positive by AI
are more likely to go for follow-up at the ECP. Aim 2 is a prospective observational trial of AI screening to
determine if AI mitigates disparities in screening, and improves the proportion of at-risk, minority and low
income, youth who go for follow-up if their AI screen is positive. If AI is shown to increase screening rates while
mitigating disparities in access to care, it has the potential to reshape screening methods.
Partnering with the parent award presents a unique opportunity to address two pressing ethical questions:
What are the ethical challenges with conducting clinical trials for AI? How do you anticipate, identify, and
address ethical problems with AI-HC before they cause harm? The supplement team has worked closely with
the parent award investigators during their F.D.A. review, in the Collaborative Community on Ophthalmic
Imaging for the F.D.A. and, from these experiences, in development of a methodology to identify and address
ethical challenges with AI for healthcare. In this supplement we will pilot our ethical analysis methodology to: 1)
identify the ethical issues emerging with clinical trials of AI-HC for DR in real time as such trials are being
conducted through the parent award; 2) develop expert consensus on how to address these identified ethical
challenges for the parent award; and 3) in doing 1 & 2, refine a generalizable approach for identifying and
addressing ethical challenges with an AI-HC and a roadmap for how to address ethical concerns with future
clinical trials of AI-HC.

## Key facts

- **NIH application ID:** 10598686
- **Project number:** 3R01EY033233-02S1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Risa Michelle Wolf
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $343,488
- **Award type:** 3
- **Project period:** 2021-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10598686

## Citation

> US National Institutes of Health, RePORTER application 10598686, Autonomous AI to mitigate disparities for diabetic retinopathy screening in youth during and after COVID-19 (3R01EY033233-02S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10598686. Licensed CC0.

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