ArticleThe British journal of ophthalmology2026
Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review.
Article in The British journal of ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Teleophthalmology and Teleglaucoma in Clinical Practice: Attitudes of Ophthalmologists in Bulgaria.Healthcare (Basel, Switzerland) · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This scoping review examines the existing literature on the application of artificial intelligence (AI) in screening for eye diseases, with a focus on evaluating whether AI-assisted diagnostic technologies enhance the availability, accessibility, acceptability and quality of screening services. 42 original studies were selected for in-depth analysis, including those employing health economic evaluations. Methodological quality was assessed using the Mixed Methods Appraisal Tool, with the majority of studies demonstrating high quality-24 scored 5/5, 15 scored 4/5 and the rest scored 3/5. Among the included studies, 34 compared manual screening with either AI-assisted or fully AI-driven approaches. Availability was the most frequently studied aspect (28 studies), followed by acceptability (12 studies), whereas accessibility and service quality were less commonly addressed. Overall, AI shows significant potential to improve the cost-effectiveness of eye care services and enhance patient access, particularly in remote or underserved regions. It was also well-accepted by patients, with high satisfaction and improved referral compliance. The findings suggest that AI holds promise for advancing eye disease screening, although large-scale, long-term trials are needed to effectively integrate AI into the reconstruction of screening processes and the reshaping of eye health service systems.
Indexed as
Identifiers
What OpenQuestion holds
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.