ArticleJournal of diabetes science and technology2022
Five-Year Cost-Effectiveness Modeling of Primary Care-Based, Nonmydriatic Automated Retinal Image Analysis Screening Among Low-Income Patients With Diabetes.
Article in Journal of diabetes science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Economic evaluation of AI-assisted technologies in healthcare: A systematic review.Journal of food and drug analysis · 2025Pooled it
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Review
- Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis.BMJ open ophthalmology · 2026Article
- Artificial intelligence for detection and staging of Alzheimer's disease using retinal images.The Cochrane database of systematic reviews · 2026Article
- The Burden of Delayed Diabetic Retinopathy Management and Use of Artificial Intelligence-Driven Screening Tools: A Systematic Literature Review.Ophthalmology and therapy · 2026Review
- Autonomous Artificial Intelligence in Diabetic Retinopathy Testing-Lessons Learned on Successful Health System Adoption.Ophthalmology science · 2026Article
- Artificial intelligence in preventive care in primary health care settings: a scoping review.Archives of medical sciences. Atherosclerotic diseases · 2026Article
- Can Multimodal Large Language Models Diagnose Diabetic Retinopathy from Fundus Photos? A Quantitative Evaluation.Ophthalmology science · 2026Article
- Application of Artificial Intelligence to Deliver Healthcare From the Eye.JAMA ophthalmology · 2025Review
- Cost-effectiveness of AI for pediatric diabetic eye exams from a health system perspective.NPJ digital medicine · 2025Article
- Autonomous Artificial Intelligence for Diabetic Eye Disease Testing Improves Access and Equity in the Pediatric and Adult Populations: The Johns Hopkins Medicine Experience.Diabetes spectrum : a publication of the American Diabetes Association · 2025Article
- Enhancing diabetic retinopathy screening: Non-mydriatic fundus photography-assisted telemedicine for improved clinical management.World journal of diabetes · 2024Article
- Advances in Structural and Functional Retinal Imaging and Biomarkers for Early Detection of Diabetic Retinopathy.Biomedicines · 2024Review
- Challenges and Potential of Artificial Intelligence in Neuroradiology.Clinical neuroradiology · 2024Review
- Artificial Intelligence Applications in Diabetic Retinopathy: What We Have Now and What to Expect in the Future.Endocrinology and metabolism (Seoul, Korea) · 2024Review
- A pilot cost-analysis study comparing AI-based EyeArt® and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway.International journal of retina and vitreous · 2024Article
- Potency of teleophthalmology as a detection tool for diabetic retinopathy.Scientific reports · 2023Article
- Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness.Diabetes care · 2023Review
- Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundArtificial intelligence-based technology systems offer an alternative solution for diabetic retinopathy (DR) screening compared with standard, in-office dilated eye examinations. We performed a cost-effectiveness analysis of Automated Retinal Image Analysis System (ARIAS)-based DR screening in a primary care medicine clinic that serves a low-income patient population.
methodsA model-based, cost-effectiveness analysis of two DR screening systems was created utilizing data from a recent study comparing adherence rates to follow-up eye care among adults ages 18 or older with a clinical diagnosis of diabetes. In the study, the patients were prescreened with an ARIAS-based, nonmydriatic (undilated), point-of-care tool in the primary care setting and were compared with patients with diabetes who were referred for dilated retinal screening without prescreening, as is the current standard of care. Using a Markov model with microsimulation resulting in a total of 600 000 simulated patient experiences, we calculated the incremental cost-utility ratio (ICUR) of the two screening approaches, with regard to five-year cost-effectiveness of DR screening and treatment of vision-threatening DR.
resultsAt five years, ARIAS-based screening showed similar utility as the standard of care screening systems. However, ARIAS reduced costs by 23.3%, with an ICUR of $258 721.81 comparing the current practice to ARIAS.
conclusionsPrimary care-based ARIAS DR screening is cost-effective when compared with standard of care screening methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.