ArticleBMC medical education2025
The potential of artificial intelligence reading label system on the training of ophthalmologists in retinal diseases, a multicenter bimodal multi-disease study.
Article in BMC medical education, 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.
- Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.Ophthalmology and therapy · 2025Review
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
objectiveTo assess the potential of artificial intelligence reading label system on the training of ophthalmologists in a multicenter bimodal multi-disease study.
methodsThe accuracy of 16 ophthalmologists with study duration ranging from one to nine years across multiple annotation rounds and its correlation with the number of rounds and ophthalmology study duration were analyzed. Additionally, this study evaluated the concordance between optical coherence tomography (OCT) or color fundus photography (CFP) and final case diagnosis.
resultsThe study involved 7777 pairs of OCT and CFP images, cases labeled with nine prevalent retinal diseases including diabetic retinopathy (DR, 2118 cases), retinal detachment (RD, 121 cases), retinal vein occlusion (RVO, 886 cases), dry age-related macular degeneration (dAMD, 549 cases), wet age-related macular degeneration (wAMD, 1023 cases), epiretinal membrane (ERM, 1061 cases), central serous retinopathy (CSC, 150 cases), macular schisis (MS, 128 cases), macular hole (MH, 86 cases) and normal fundus (1036 cases) were selected for further analysis. All images were assigned to 16 ophthalmologists over five rounds. The average diagnostic accuracy for the nine retinal diseases and normal fundus improved significantly across the five rounds (p = 0.013) and is closely correlated to the duration of ophthalmology study (p = 0.007). Furthermore, significant improvements were observed in the diagnostic accuracy of both OCT (p = 0.028) and CFP (p = 0.021) modalities as the number of rounds increased. Notably, OCT single modal diagnosis demonstrated higher consistency with the final diagnosis in cases of RD, ERM, MS, and MH compared to CFP, while CFP single modal diagnosis has higher consistency in DR, RVO and normal fundus.
conclusionThe implementation of an artificial intelligence reading label system enhances the diagnostic accuracy of retinal diseases among ophthalmologists and holds potential for integration into future medical education.
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.