ReviewGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2023
Artificial intelligence in retinal disease: clinical application, challenges, and future directions.
Review in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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
41 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Adult-onset Coats disease: A systematic review and meta-analysis of imaging biomarkers and targeted therapies.Acta ophthalmologica · 2026Pooled it
- Diagnostic accuracy of computer-aided detection for colorectal polyps of any size, ≤ 5 mm, and 6-9 mm: a meta-analysis.World journal of surgical oncology · 2026Pooled it
- Current Applications of Artificial Intelligence for Fuchs Endothelial Corneal Dystrophy: A Systematic Review.Translational vision science & technology · 2025Pooled it
- Intelligent Visual Prioritization for Retinal Prostheses via Context-Aware Object Ranking and Depth-Aware Phosphene Generation.Biomimetics (Basel, Switzerland) · 2026Article
- A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.iScience · 2026Article
- Deep Feature-Based Normality Modeling for Automated Out-Of-Distribution Detection in Sheep Retinal Fundus Images.Veterinary ophthalmology · 2026Article
- Interpretability of multimodal neural networks for prediction of visual acuity in patients with branch retinal vein occlusion.Scientific reports · 2026Article
- Interoperable Integration of a National Rare Disease Registry Into a Rare Eye Disease Data Warehouse: Implementation Study.JMIR medical informatics · 2026Article
- Diagnostic Agreement Between a General-Purpose AI Model and Retinal Specialists in Color Fundus Photography-A Pilot Study.Journal of clinical medicine · 2026Article
- An Update on Retinal Diseases: From Diagnosis to Treatment.Journal of clinical medicine · 2026Article
- Review
- Visual Field Estimation in X-Linked Retinitis Pigmentosa Associated with Retinitis Pigmentosa GTPase Regulator (Ophthalmology science · 2026Article
- Evaluation of classification performance for six types of fundus diseases in OCT images based on multi-source training strategy.Frontiers in medicine · 2026Article
- The Clinical Significance of Imaging Biomarkers Discoverable by Anomaly Detection Methods in Retinal Diseases: A Review.Journal of ophthalmology · 2026Review
- Development of a deep learning model to classify choroidal melanoma risk factors based on color fundus photographs.AJO international · 2025Article
- The Role of Artificial Intelligence in Imaging-Based Diagnosis of Retinal Dystrophy and Evaluation of Gene Therapy Efficacy.Journal of personalized medicine · 2025Review
- A robust deep learning classifier for screening multiple retinal diseases on optical coherence tomography.Scientific reports · 2025Article
- Artificial Intelligence in Ophthalmology: Acceptance, Clinical Integration, and Educational Needs in Switzerland.Journal of clinical medicine · 2025Article
- Ensemble-based eye disease detection system utilizing fundus and vascular structures.Scientific reports · 2025Article
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 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
7 authors.
Funding
Abstract
Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.
Indexed as
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What OpenQuestion 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 OpenQuestion graph.