ArticleInternational journal of ophthalmology2023
Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023).
Article in International journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 3 of them syntheses that pooled it.
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Who cites it
67 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Recent advances and applications of optical coherence tomography angiography in diabetic retinopathy.Frontiers in endocrinology · 2025Pooled it
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade.Frontiers in medicine · 2024Pooled it
- Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images.Frontiers in artificial intelligence · 2026Trial
- TeleGlaukos: A Clinician-Led Adaptive Telemonitoring Platform for Hybrid Glaucoma Care.Bioengineering (Basel, Switzerland) · 2026Article
- Comparative Performance of Gemini 3 Pro and GPT-5 Family Models on Ophthalmology Board-Style Questions.Ophthalmology science · 2026Article
- An explainable ensemble for diabetic retinopathy grading with a novel confidence quality factor and configurable heatmaps.Medical & biological engineering & computing · 2026Article
- Artificial intelligence-based diagnostic model construction and quantitative analysis of retinal vein occlusion lesions using color fundus photographs.Frontiers in cell and developmental biology · 2026Article
- Learning ophthalmic anatomy with AI-generated visual resource: the moderating role of educational background.Frontiers in medicine · 2026Article
- Association between fundus tessellated density and subfoveal choroidal thickness in children with different refractive statuses.Frontiers in medicine · 2026Article
- An intelligent segmentation method for leakage points in central serous chorioretinopathy based on fluorescein angiography images.International journal of ophthalmology · 2026Article
- Digital health technologies for the management of sarcopenia in patients receiving maintenance hemodialysis: a narrative review.Frontiers in nutrition · 2026Review
- Using deep learning to identify inherited retinal diseases based on wide-field retinal imaging data.PloS one · 2026Article
- Risk-centered benchmarking of large language models for AI-enabled counseling in chronic autoimmune thyroid eye disease.Frontiers in cell and developmental biology · 2026Article
- Benchmarking publicly accessible large language models for high-myopia multiple-choice question generation in digital ophthalmic education and public health training.Frontiers in public health · 2026Article
- TransDualSegNet: transformer dual segment network for retinal vasculature segmentation in OCT.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence applications in refractive error management: A systematic review and meta-analysis.PLOS digital health · 2025Article
- Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.Ophthalmology and therapy · 2025Review
- A Novel System for Measuring Eyeball Rotation Angle Based on Color Fundus Photographs in Natural Head Position.Translational vision science & technology · 2025Article
- An Intelligent Grading Model for Myopic Maculopathy Based on Long-Tailed Learning.Translational vision science & technology · 2025Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
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Abstract
With the upsurge of artificial intelligence (AI) technology in the medical field, its application in ophthalmology has become a cutting-edge research field. Notably, machine learning techniques have shown remarkable achievements in diagnosing, intervening, and predicting ophthalmic diseases. To meet the requirements of clinical research and fit the actual progress of clinical diagnosis and treatment of ophthalmic AI, the Ophthalmic Imaging and Intelligent Medicine Branch and the Intelligent Medicine Committee of Chinese Medicine Education Association organized experts to integrate recent evaluation reports of clinical AI research at home and abroad and formed a guideline on clinical research evaluation of AI in ophthalmology after several rounds of discussion and modification. The main content includes the background and method of developing this guideline, an introduction to international guidelines on the clinical research evaluation of AI, and the evaluation methods of clinical ophthalmic AI models. This guideline introduces general evaluation methods of clinical ophthalmic AI research, evaluation methods of clinical ophthalmic AI models, and commonly-used indices and formulae for clinical ophthalmic AI model evaluation in detail, and amply elaborates the evaluation methods of clinical ophthalmic AI trials. This guideline aims to provide guidance and norms for clinical researchers of ophthalmic AI, promote the development of regularization and standardization, and further improve the overall level of clinical ophthalmic AI research evaluations.
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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.