ReviewClinical & experimental ophthalmology
Health Economic Considerations for the Implementation of Artificial Intelligence-Enabled Diabetic Retinopathy Screening: A Review.
Review in Clinical & experimental ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Real-world performance of an AI system for diabetic retinopathy screening.Scientific reports · 2026Article
- Global perspectives of ophthalmologists on artificial intelligence adoption in clinical practice.International journal of retina and vitreous · 2025Article
- 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has comparable accuracy to ophthalmologists for diabetic retinopathy (DR) screening, yet its cost-effectiveness is crucial for implementation. Our review of 18 health economic analyses of AI versus manual grading for DR found significant methodological variation, with cost-utility analysis and Markov modelling being the commonest evaluation and modelling approaches, respectively. We identified three key considerations when appraising health economic analyses of AI-enabled DR screening: the importance of contextualised parameters including subgroup analysis, real-world data on adherence to ophthalmology follow-up, and the trade-off between diagnostic accuracy and cost-effectiveness. 39% of studies followed standardised reporting guidelines, and most did not consider improved follow-up after AI screening, potentially underestimating its economic value. Future evaluations should incorporate contextualised parameters, including adherence and regional data, and recognise that the most accurate diagnostic screening may not reflect the most cost-effective. Studies should follow updated reporting guidelines such as CHEERS-AI or PICOTS-ComTeC to improve methodological transparency.
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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.