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Autonomous Artificial Intelligence in Diabetic Retinopathy Testing-Lessons Learned on Successful Health System
Clare W Teng1, Saawan D Patel1, Andrew J Barkmeier2
1Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Purpose:
Artificial intelligence (AI)-aided diabetic retinopathy (DR) testing systems have been commercialized for 5 years, but adoption is still relatively limited. This article aims to summarize the evidence in clinical settings, describe the current state of adoption, and share themes of successful implementation.
Design:
Evaluation of diagnostic test or technology.
Participants:
Ophthalmologists.
Methods:
We performed literature review and conducted interviews with ophthalmologists leading implementation of AI-aided DR testing programs at several academic health systems. The study focused on the 3 currently US Food and Drug Administration-cleared AI systems: LumineticsCore, EyeArt, and AEYE Diagnostic Screening (AEYE-DS), assessing their performance and strategies utilized by health systems to effectively implement this technology in clinics.
Main Outcome Measures:
Diagnostic accuracy data, ophthalmologist feedback.
Results:
The literature review found 6 publications reporting diagnostic accuracy data of autonomous AI DR testing in primary care office settings, including 5 for LumineticsCore and 1 for EyeArt. Additional articles, of which 18 were selected for detailed review, addressed impact on patient adherence, health equity, and carbon footprint, as well as cost-effectiveness and workflow efficiency analyses. There were no studies comparing the systems on the same patients. In aggregate, adopters of the AI systems reported average nonmydriatic gradability of 49% to 75% (n = 5), sensitivity 87% to 100% (n = 3), and specificity 60% to 91% (n = 4). Based on public records at the time of writing, both LumineticsCore and EyeArt have >5 academic adopters in the United States. Limited information is available on AEYE-DS given recency of regulatory clearance. Elements of successful implementation include proper site selection, aligning AI tools with primary care clinic workflows, streamlining patient engagement and referrals, and ongoing training of staff. Health systems utilizing AI reported improved Healthcare Effectiveness Data and Information Set measures, health equity, productivity, and patient adherence to follow-up with ophthalmology.
Conclusions:
Artificial intelligence-aided diabetic eye examinations present a promising solution to facilitate early detection of DR, promote equitable access, and drive down system-level cost of care. Its successful implementation requires addressing technological, operational, and stakeholder engagement challenges. Our study underscores the potential of AI to revolutionize care delivery provided its adoption is strategically managed.
Financial Disclosures:
The author has no/the authors have no proprietary or commercial interest in any materials discussed in this article.
