ArticleBMC medical education2022
Using artificial intelligence reading label system in diabetic retinopathy grading training of junior ophthalmology residents and medical students.
Article in BMC medical education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Application value of an artificial intelligence-based diagnosis and recognition system in gastroscopy training for graduate students in gastroenterology: a preliminary study.Wiener medizinische Wochenschrift (1946) · 2024Trial
- Can clinical decision support systems be an asset in medical education? An experimental approach.BMC medical education · 2023Trial
- Integrated Implementation Strategies to Promote the Use of AI-Assisted Diagnostic Software for Lung Nodule Screening in China: Process Evaluation Based on the RE-AIM Framework.JMIR formative research · 2026Article
- Effect of AI-driven simulation integrated with virtual case-based training on diagnostic and therapeutic reasoning homogenization among junior ophthalmologists: a prospective cohort study.Frontiers in medicine · 2026Article
- Artificial intelligence-based apps for screening and diagnosing diabetic retinopathy and common ocular disorders.World journal of methodology · 2025Review
- Bridging Artificial Intelligence and Medical Education: Navigating the Alignment Paradox.ATS scholar · 2025Review
- Synergistic AI-resident approach achieves superior diagnostic accuracy in tertiary ophthalmic care for glaucoma and retinal disease.Frontiers in ophthalmology · 2025Article
- Impact of Artificial Intelligence on the Knowledge, Attitude, and Performance of Ophthalmology Residents: A Systematic Review.Journal of ophthalmic & vision research · 2025Article
- A Beginner's Guide to Artificial Intelligence for Ophthalmologists.Ophthalmology and therapy · 2024Review
- The application of novel techniques in ophthalmology education.Frontiers in medicine · 2024Review
- Deep learning in optical coherence tomography: Where are the gaps?Clinical & experimental ophthalmology · 2023Review
- Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program.Frontiers in cell and developmental biology · 2022Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeEvaluate the efficiency of using an artificial intelligence reading label system in the diabetic retinopathy grading training of junior ophthalmology resident doctors and medical students.
methodsLoading 520 diabetic retinopathy patients' colour fundus images into the artificial intelligence reading label system. Thirteen participants, including six junior ophthalmology residents and seven medical students, read the images randomly for eight rounds. They evaluated the grading of images and labeled the typical lesions. The sensitivity, specificity, and kappa scores were determined by comparison with the participants' results and diagnosis gold standards.
resultsThrough eight rounds of reading, the average kappa score was elevated from 0.67 to 0.81. The average kappa score for rounds 1 to 4 was 0.77, and the average kappa score for rounds 5 to 8 was 0.81. The participants were divided into two groups. The participants in Group 1 were junior ophthalmology resident doctors, and the participants in Group 2 were medical students. The average kappa score of Group 1 was elevated from 0.71 to 0.76. The average kappa score of Group 2 was elevated from 0.63 to 0.84.
conclusionThe artificial intelligence reading label system is a valuable tool for training resident doctors and medical students in performing diabetic retinopathy grading.
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