Peer-Reviewed Publication
NPJ Digit Med2025;8(1):3.January 2, 2025Journal Article
Cost-effectiveness of AI for pediatric diabetic eye exams from a health system perspective.
Mahnoor Ahmed1, Tinglong Dai2,3,4, Roomasa Channa5, Michael D Abramoff6,7,8,9,10, Harold P Lehmann1, Risa M Wolf11,12
1Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA.
2Carey Business School, Johns Hopkins University, Baltimore, MD, USA.
3Hopkins Business of Health Initiative, Johns Hopkins University, Baltimore, MD, USA.
4School of Nursing, Johns Hopkins University, Baltimore, MD, USA.
5Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA.
6Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA.
7Digital Diagnostics Inc, Coralville, IA, USA.
8Iowa City VA Medical Center, Iowa City, IA, USA.
9Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA.
10Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA.
11Hopkins Business of Health Initiative, Johns Hopkins University, Baltimore, MD, USA. RWolf@jhu.edu.
12Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA. RWolf@jhu.edu.
Abstract
Autonomous artificial intelligence (AI) for pediatric diabetic retinal disease (DRD) screening has demonstrated safety, effectiveness, and the potential to enhance health equity and clinician productivity. We examined the cost-effectiveness of an autonomous AI strategy versus a traditional eye care provider (ECP) strategy during the initial year of implementation from a health system perspective.…
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