SynthesisEye (London, England)2025
Artificial Intelligence improves follow-up appointment uptake for diabetic retinal assessment: a systematic review and meta-analysis.
Synthesis in Eye (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed.
- Artificial Intelligence in Oral and Maxillofacial Surgery-A Narrative Review.Journal of clinical medicine · 2026Review
- Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Severity of hypertensive retinopathy and its determinants among adult patients attending ophthalmic centers in Northern Ethiopia: A multicenter cross-sectional study.BMC ophthalmology · 2026Article
- Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Review
- Editorial: Mental health in the age of artificial intelligence.Frontiers in psychiatry · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesArtificial intelligence (AI) assessment of diabetic retinopathy (DR) instead of scarce trained specialists could potentially increases the efficiency and accessibility of screening programs. This systematic review aims to systematically examine the uptake of follow-up appointments with initial computer-based AI and human graders of DR.
methodsWe conducted a systematic review and meta-analysis by screening articles in any languages in PubMed, MEDLINE (Ovid), EMBASE, Web of Science, Cochrane CENTRAL and CDSR published from database inception up to 20
resultsData from a total of 20,108 patients with diabetes (6476 participants graded using AI and 13,632 participants graded by human-graders; age range of the participants 5 to 67 years) from six studies were included. The result of the pooled meta-analysis showed that initial AI assessment of DR significantly increased uptake of follow-up appointments compared to human grader-based (OR = 1.89, 95% CI 1.78-2.01, P = 0.00001).
conclusionsThe present systematic review and meta-analysis suggest that initial AI-based algorithm for screening DR is associated with an increased uptake of follow-up examination. This is most likely due to instant results being made available with AI based algorithms when compared to a delay in assessment with human graders.
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Registered trials
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