ReviewBioengineering (Basel, Switzerland)2025
Artificial Intelligence in Diabetic Retinopathy and Diabetic Macular Edema: A Narrative Review.
Review in Bioengineering (Basel, Switzerland), 2025. 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.
- Early Detection of Glaucoma and Diabetic Retinopathy in Low-Resource Settings: Barriers and Solutions.Journal of clinical medicine · 2026Review
- Albuminuria, but not eGFR, tracks diabetic retinopathy severity and retinal ischemia: population-based discovery, clinical replication, and OCTA evidence.Frontiers in endocrinology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) and diabetic macular edema (DME) remain major causes of vision loss among working-age adults. Artificial intelligence (AI), particularly deep learning, has gained attention in ophthalmic imaging, offering opportunities to improve both diagnostic accuracy and efficiency. This review examined applications of AI in DR and DME published between 2010 and 2025. A narrative search of PubMed and Google Scholar identified English-language, peer-reviewed studies, with additional screening of reference lists. Eligible articles evaluated AI algorithms for detection, classification, prognosis, or treatment monitoring, with study selection guided by PRISMA 2020. Of 300 records screened, 60 met the inclusion criteria. Most reported strong diagnostic performance, with sensitivities up to 96% and specificities up to 98% for detecting referable DR on fundus photographs. Algorithms trained on optical coherence tomography (OCT) data showed high accuracy for identifying DME, with area under the receiver operating characteristic curve (AUC) values frequently exceeding 0.90. Several models also predicted anti-vascular endothelial growth factor (anti-VEGF) treatment response and recurrence of fluid with encouraging results. Autonomous AI tools have gained regulatory approval and have been implemented in clinical practice, though performance can vary depending on image quality, device differences, and patient populations. Overall, AI demonstrates strong potential to improve screening, diagnostic consistency, and personalized care, but broader validation and system-level integration remain necessary.
Indexed as
Identifiers
What Socratic 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 Socratic graph.