ArticleOphthalmology and therapy2023
Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program.
Article in Ophthalmology and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
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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.
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Who cites it
20 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Economic evaluation of AI-assisted technologies in healthcare: A systematic review.Journal of food and drug analysis · 2025Pooled it
- Economic Evaluation of Diabetic Retinopathy Screening in Developing Countries: A Systematic Review.Korean journal of ophthalmology : KJO · 2025Pooled it
- Approaches to economic evaluations of complex interventions in thailand: a systematic review.BMC public health · 2025Pooled it
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Practical lessons in the global scaling of clinical AI: from one hospital to over a million patients screened.Nature medicine · 2026Article
- Prospective Data Curation Enables High-Performance Artificial Intelligence for Diabetic Retinopathy Screening in a Resource-Limited Setting.Ophthalmology science · 2026Article
- Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review.The British journal of ophthalmology · 2026Article
- National implementation of a digital Parkinson's disease screening programme in Thailand: reach, adoption, and real-world performance of the CheckPD app.BMC public health · 2026Article
- The Burden of Delayed Diabetic Retinopathy Management and Use of Artificial Intelligence-Driven Screening Tools: A Systematic Literature Review.Ophthalmology and therapy · 2026Review
- Prospective real-world implementation of deep learning systems in healthcare: a systematic review guided by implementation science.NPJ digital medicine · 2026Article
- A Narrative Review of Ethical Issues in the Use of Artificial Intelligence Enabled Diagnostics for Diabetic Retinopathy.Journal of evaluation in clinical practice · 2025Review
- Applications of artificial intelligence and the challenges in health technology assessment: a scoping review and framework with a focus on economic dimensions.Health economics review · 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
- Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population.Diabetology & metabolic syndrome · 2024Article
- 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
- Review
- Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness.Diabetes care · 2023Review
- Review
- Health Economic Considerations for the Implementation of Artificial Intelligence-Enabled Diabetic Retinopathy Screening: A Review.Clinical & experimental ophthalmologyReview
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDeep learning (DL) for screening diabetic retinopathy (DR) has the potential to address limited healthcare resources by enabling expanded access to healthcare. However, there is still limited health economic evaluation, particularly in low- and middle-income countries, on this subject to aid decision-making for DL adoption.
methodsIn the context of a middle-income country (MIC), using Thailand as a model, we constructed a decision tree-Markov hybrid model to estimate lifetime costs and outcomes of Thailand's national DR screening program via DL and trained human graders (HG). We calculated the incremental cost-effectiveness ratio (ICER) between the two strategies. Sensitivity analyses were performed to probe the influence of modeling parameters.
resultsFrom a societal perspective, screening with DL was associated with a reduction in costs of ~ US$ 2.70, similar quality-adjusted life-years (QALY) of + 0.0043, and an incremental net monetary benefit of ~ US$ 24.10 in the base case. In sensitivity analysis, DL remained cost-effective even with a price increase from US$ 1.00 to US$ 4.00 per patient at a Thai willingness-to-pay threshold of ~ US$ 4.997 per QALY gained. When further incorporating recent findings suggesting improved compliance to treatment referral with DL, our analysis models effectiveness benefits of ~ US$ 20 to US$ 50 depending on compliance.
conclusionDR screening using DL in an MIC using Thailand as a model may result in societal cost-savings and similar health outcomes compared with HG. This study may provide an economic rationale to expand DL-based DR screening in MICs as an alternative solution for limited availability of skilled human resources for primary screening, particularly in MICs with similar prevalence of diabetes and low compliance to referrals for treatment.
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