ReviewAdvances in ophthalmology practice and research
Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives.
Review in Advances in ophthalmology practice and research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 4 of them syntheses that pooled it.
What it found
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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
76 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Multimodal artificial intelligence in retinal vascular and neovascular macular diseases: a systematic review of diagnostic and prognostic applications.BMC ophthalmology · 2025Pooled it
- Economic Evaluation of Diabetic Retinopathy Screening in Developing Countries: A Systematic Review.Korean journal of ophthalmology : KJO · 2025Pooled it
- Next-gen vision: a systematic review on robotics transforming ophthalmic surgery.Journal of robotic surgery · 2025Pooled it
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Fellow-eye retinal age gap and high injection burden over four years in neovascular age-related macular degeneration.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- 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
- Clinical Performance and Implementation of AI-Enabled Paediatric Ophthalmic Screening, Triage, Diagnosis, and Surveillance in Primary, Community, and Referral-Linked Pathways: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Early Detection of Glaucoma and Diabetic Retinopathy in Low-Resource Settings: Barriers and Solutions.Journal of clinical medicine · 2026Review
- A Deep Learning-Based Study on Automatic Measurement Method of Biological Parameters in Anterior Segment UBM Images of Angle-Closure Glaucoma.Translational vision science & technology · 2026Article
- Advancing AMD Detection: Dataset Design and Deep Learning Optimization for Unconstrained Retinal Images.Vision (Basel, Switzerland) · 2026Article
- Rethinking scale in ophthalmic artificial intelligence: from bigger models to smarter clinical reasoning.NPJ digital medicine · 2026Review
- Artificial Intelligence-Based Medical Devices for Diabetic Retinopathy Screening in the European Union.Ophthalmology and therapy · 2026Review
- COL1A2 and APOLD1 Define a Dual-Axis Molecular Framework for Diabetic Nephropathy-Retinopathy Comorbidity: An Integrative Multiomics and Machine Learning Study in Mouse Models.International journal of medical sciences · 2026Article
- Benchmarking Vision Encoders for Image Classification in Ophthalmology.Computational and structural biotechnology journal · 2026Article
- AI-Based Visual Prognosis in Full-Thickness Macular Hole Surgery Using the ILM Flap Technique.Beyoglu eye journal · 2026Article
- Benchmark evaluation of multi-modal large language models for ophthalmic diagnosis in real world.Frontiers in medicine · 2026Article
- Why aren't we using AI in eye clinics? A systematic review of barriers and solutions in AI-based fundus image diagnostics for ocular diseases.International journal of ophthalmology · 2026Review
- Evaluating multimodal large language models for differential diagnosis of high myopia versus high myopia with Glaucoma.Frontiers in medicine · 2026Article
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 2026Review
- An intelligent segmentation method for leakage points in central serous chorioretinopathy based on fluorescein angiography images.International journal of ophthalmology · 2026Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic. Main text: At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns. Conclusions: This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.
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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.