ArticleFrontiers in public health2022
The effectiveness of artificial intelligence-based automated grading and training system in education of manual detection of diabetic retinopathy.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trends and hotspots in the field of diabetic retinopathy imaging research from 2000-2023.Frontiers in medicine · 2024Pooled it
- Development and implementation of a novel clinical teaching model integrating real-world case with patient-specific three-dimensional imaging in urology.BMC medical education · 2026Trial
- 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
- 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
- What and when: a Delphi study identifying AI competencies and learning objectives for medical students.Frontiers in digital health · 2026Article
- Application of an AI-enhanced interactive virtual clinical reasoning training system in orthodontic residency training.Frontiers in medicine · 2026Article
- Evaluating the diagnostic performance of artificial intelligence-assisted decision-making software for pulmonary nodules in a resource-limited setting.Journal of clinical imaging science · 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
- Medical Education and Artificial Intelligence: Web of Science-Based Bibliometric Analysis (2013-2022).JMIR medical education · 2024Article
- Research progress in artificial intelligence assisted diabetic retinopathy diagnosis.International journal of ophthalmology · 2023Article
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13 authors.
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Abstract
Background: The purpose of this study is to develop an artificial intelligence (AI)-based automated diabetic retinopathy (DR) grading and training system from a real-world diabetic dataset of China, and in particular, to investigate its effectiveness as a learning tool of DR manual grading for medical students. Methods: We developed an automated DR grading and training system equipped with an AI-driven diagnosis algorithm to highlight highly prognostic related regions in the input image. Less experienced prospective physicians received pre- and post-training tests by the AI diagnosis platform. Then, changes in the diagnostic accuracy of the participants were evaluated. Results: We randomly selected 8,063 cases diagnosed with DR and 7,925 with non-DR fundus images from type 2 diabetes patients. The automated DR grading system we developed achieved accuracy, sensitivity/specificity, and AUC values of 0.965, 0.965/0.966, and 0.980 for moderate or worse DR (95 percent CI: 0.976-0.984). When the graders received assistance from the output of the AI system, the metrics were enhanced in varying degrees. The automated DR grading system helped to improve the accuracy of human graders, i.e., junior residents and medical students, from 0.947 and 0.915 to 0.978 and 0.954, respectively. Conclusion: The AI-based systemdemonstrated high diagnostic accuracy for the detection of DR on fundus images from real-world diabetics, and could be utilized as a training aid system for trainees lacking formal instruction on DR management.
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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.