SynthesisFrontiers in medicine2026
Effectiveness of screening modalities for early detection of diabetic retinopathy: a systematic review and meta-analysis of tele-ophthalmology, AI-based tools, and conventional methods.
Synthesis in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Barriers and enablers for health system reform.Frontiers in health services · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Diabetic retinopathy (DR) causes severe vision impairment that requires early screening methods for effective detection. The combination of Artificial Intelligence (AI) and tele-ophthalmology technology provides an effective solution that enhances both DR detection rates and patient access to care. Objective: The study aims to assess how well AI-based telemedicine smartphone conventional and population-based screening methods detect diabetic retinopathy in terms of effectiveness, accuracy, and real-world performance. Methodology: Researchers executed a comprehensive literature search across PubMed, Scopus, Web of Science, and Embase to find articles published between 2012 and 2025. The study examined three types of studies: AI-based DR screening, tele-ophthalmology, and evaluations of standard diagnostic methods. The analysis used random-effects meta-analysis to estimate pooled odds ratios (ORs), assess heterogeneity using I Results: The study included 45 different research studies. The standalone deep learning/AI tools (6 studies) demonstrated a pooled OR of 5.79 (95% CI: 5.22-6.42; Conclusion: AI-based screening, including deep learning algorithms, automated grading, and tele-ophthalmology, shows high diagnostic accuracy and consistent effectiveness. The implementation of smartphone- and population-based approaches is feasible, yet their outcomes vary, necessitating validation through context-specific assessments.
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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.