Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes
Open accessAbstract
Importance: Screening for diabetic retinopathy is recommended for children with type 1 diabetes (T1D) and type 2 diabetes (T2D), yet screening rates remain low. Point-of-care diabetic retinopathy screening using autonomous artificial intelligence (AI) has become available, providing immediate results in the clinic setting, but the cost-effectiveness of this strategy compared with standard examination is unknown. Objective: To assess the cost-effectiveness of detecting and treating diabetic retinopathy and its sequelae among children with T1D and T2D using AI diabetic retinopathy screening vs standard screening by an eye care professional (ECP). Design, Setting, and Participants: In this economic evaluation, parameter estimates were obtained from the literature from 1994 to 2019 and assessed from March 2019 to January 2020. Parameters included out-of-pocket cost for autonomous AI screening, ophthalmology visits, and treating diabetic retinopathy; probability of undergoing standard retinal examination; relative odds of undergoing screening; and sensitivity, specificity, and diagnosability of the ECP screening examination and autonomous AI screening. Main Outcomes and Measures: Costs or savings to the patient based on mean patient payment for diabetic retinopathy screening examination and cost-effectiveness based on costs or savings associated with the number of true-positive results identified by diabetic retinopathy screening. Results: In this study, the expected true-positive proportions for standard ophthalmologic screening by an ECP were 0.006 for T1D and 0.01 for T2D, and the expected true-positive proportions for autonomous AI were 0.03 for T1D and 0.04 for T2D. The base case scenario of 20% adherence estimated that use of autonomous AI would result in a higher mean patient payment ($8.52 for T1D and $10.85 for T2D) than conventional ECP screening ($7.91 for T1D and $8.20 for T2D). However, autonomous AI screening was the preferred strategy when at least 23% of patients adhered to diabetic retinopathy screening. Conclusions and Relevance: These results suggest that point-of-care diabetic retinopathy screening using autonomous AI systems is effective and cost saving for children with diabetes and their caregivers at recommended adherence rates.
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This system can be implemented in a multidisciplinary diabetes clinic and has the potential to increase DED screening rates in underserved youth, while also being cost-savings to patients and caregivers.
“We have also shown that this system can be implemented in a multidisciplinary diabetes clinic and has the potential to increase DED screening rates in underserved youth, while also being cost-savings to patients and caregivers30,31.”
From Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial (2024) · cites this paper as [31]
The Discussion (page unavailable) says point-of-care screening can be performed in a diabetes clinic, potentially increasing screening and sparing families another visit. The Results (page unavailable) find patient cost savings only when adherence to conventional eye examinations reaches at least 23%; at baseline, modeled patient payments were higher with AI screening. The paper does not establish implementation in a multidisciplinary clinic or specifically assess underserved youth. (LLM, conf=0.85)
Experimental — accuracy not yet measuredUsing autonomous AI for diabetic retinopathy screening has been shown to be lower cost in other countries.
“using autonomous AI for DR screening has been shown to be lower cost in other countries.13–16”
From Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis (2026) · cites this paper as [15]
The Results report that autonomous AI screening was cost-saving compared with eye care professional screening when adherence exceeded 23%, but not in the 20% baseline scenario. The Discussion places the pediatric patient-and-caregiver model in a US context; it does not establish lower costs in other countries. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAI systems are increasingly in a position to help improve patient and population health outcomes and drive down cost, increase physician job satisfaction, and address health disparities.
“Thus, AI systems are increasingly in a position to help improve patient and population health outcomes and drive down cost, increase physician job satisfaction, and address health disparities20,21,3,22,23.”
From Considerations for addressing bias in artificial intelligence for health equity (2023) · cites this paper as [23]
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References 38 · Cited by 87
- Authors
- Wolf, R. M., Channa, R., Abramoff, M. D., & Lehmann, H. P.
- Affiliations
- Johns Hopkins University (US)
- Baylor College of Medicine (US)
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City
- Corresponding author
- Wolf, Risa M.
- Venue
- JAMA Ophthalmology
- Publisher
- American Medical Association (AMA)
- Published
- 2020-10-01
- Volume
- 138
- Issue
- 10
- Pages
- 1063
- ISSN
- 2168-6165 (ISSN-L)
- PMC
- PMC7489415
- OpenAlex
- W3082547533
- Cited by
- 87
- References
- 38
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 17 · 2023: 16 · 2022: 15 · 2021: 10 · 2020: 1
- FWCI
- 5.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Ophthalmology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Keywords
- diabetic retinopathy, point-of-care screening, AI autonomy, pediatric diabetes, type 1 diabetes, type 2 diabetes, cost-effectiveness, screening adherence, ophthalmologic examination, patient payment, retinal imaging
- MeSH terms
- Adolescent, Artificial Intelligence, Child, Cost-Benefit Analysis, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Diabetic Retinopathy, Female, Humans, Male, Mass Screening, Retrospective Studies, Point-of-Care Systems, Young Adult
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