ReviewCell reports. Medicine2023
Artificial intelligence in ophthalmology: The path to the real-world clinic.
Review in Cell reports. Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 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
110 citing papers in PubMed, 3 syntheses or guidelines pooled it, 148 citations in OpenAlex.
- Can artificial intelligence with multimodal imaging outperform traditional methods in predicting age-related macular degeneration progression? A systematic review and exploratory meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.La Radiologia medica · 2025Pooled it
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application.World journal of methodology · 2026Review
- Multimodal AI in retinal disease: from images to decisions and real-world endpoints.International ophthalmology · 2026Article
- Artificial Intelligence for Predicting Postoperative Outcomes in Anterior Segment and Ocular Adnexa Surgery: A Systematic Review.Ophthalmology science · 2026Review
- From generalization to precision: A large domain-specific pretrained model for specialized medical tasks.Cell reports. Medicine · 2026Article
- Can large language models accurately triage oculoplastic referrals?Eye (London, England) · 2026Article
- Imaging-based machine learning for the diagnosis and prognosis of uveal melanoma: a systematic review and meta analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Dual branch fundus deep learning network as an enhanced multi classification system for ocular disease detection via hybrid feature fusion.Scientific reports · 2026Article
- Evaluation of the Efficacy of Artificial Intelligence-Based Screening for Diabetic Retinopathy in a Predominantly Non-White Population.Southern medical journal · 2026Article
- Development and validation of the IVENUS artificial intelligence model for automated CEAP classification of early-stage chronic venous disease using lower limb photographs.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Neural Vision Restoration in Ophthalmology.Annals of biomedical engineering · 2026Review
- Rethinking scale in ophthalmic artificial intelligence: from bigger models to smarter clinical reasoning.NPJ digital medicine · 2026Review
- Deep learning detection of retinitis pigmentosa inheritance forms through synthetic data expansion of a rare disease dataset.Scientific reports · 2026Article
- Practical Challenges for the Implementation of AI-Based Image Analysis in Ophthalmology Research: Insights and Recommendations from a Swiss Multicentric Study.Klinische Monatsblatter fur Augenheilkunde · 2026Article
- Artificial intelligence in retinal care: transforming the doctor-patient partnership.International journal of retina and vitreous · 2026Review
- Recent advances in corneal specular microscopy image analysis through artificial intelligence.PLOS digital health · 2026Review
- Retinal thickness and microvascular alterations in the diagnosis of behcet's disease: a case-control study.BMC medical imaging · 2026Article
- ROFI: a deep learning-based ophthalmic sign-preserving and reversible patient face anonymizer.Annals of medicine and surgery (2012) · 2026Article
50 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
10 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.
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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.