ReviewExperimental and therapeutic medicine2020
Artificial intelligence and deep learning in ophthalmology - present and future (Review).
Review in Experimental and therapeutic medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 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
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- Artificial intelligence in pediatric myopia - a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Evaluation of large language models and retrieval-augmented generation for clinical reasoning in pediatric myopia: a 50-case real-world study.Scientific reports · 2026Article
- PDC-DETR: a Parallel Dilated Convolutional Detection Transformer for preliminary screening of multiple macular degeneration.BMC medical imaging · 2025Article
- Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.Cureus · 2025Review
- Exploring AI's Potential in Papilledema Diagnosis to Support Dermatological Treatment Decisions in Rural Healthcare.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only.Scientific reports · 2025Article
- Evaluating deep learning models for classifying OCT images with limited data and noisy labels.Scientific reports · 2024Article
- Automated Identification of Clinically Relevant Regions in Glaucoma OCT Reports Using Expert Eye Tracking Data and Deep Learning.Translational vision science & technology · 2024Article
- Article
- A Beginner's Guide to Artificial Intelligence for Ophthalmologists.Ophthalmology and therapy · 2024Review
- Knowledge, attitude, and practice of artificial intelligence among medical students in Sudan: a cross-sectional study.Annals of medicine and surgery (2012) · 2024Article
- Exploring clinical specialists' perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks.BMC health services research · 2024Article
- Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery.Scientific reports · 2024Article
- Macular edema in retinal fundus images by a computational algorithmRevista medica del Instituto Mexicano del Seguro Social · 2024Article
- Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device.Diagnostics (Basel, Switzerland) · 2024Article
- Applying deep learning to recognize the properties of vitreous opacity in ophthalmic ultrasound images.Eye (London, England) · 2024Article
- Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis.International journal of ophthalmology · 2024Article
- From the diagnosis of infectious keratitis to discriminating fungal subtypes; a deep learning-based study.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since its introduction in 1959, artificial intelligence technology has evolved rapidly and helped benefit research, industries and medicine. Deep learning, as a process of artificial intelligence (AI) is used in ophthalmology for data analysis, segmentation, automated diagnosis and possible outcome predictions. The association of deep learning and optical coherence tomography (OCT) technologies has proven reliable for the detection of retinal diseases and improving the diagnostic performance of the eye's posterior segment diseases. This review explored the possibility of implementing and using AI in establishing the diagnosis of retinal disorders. The benefits and limitations of AI in the field of retinal disease medical management were investigated by analyzing the most recent literature data. Furthermore, the future trends of AI involvement in ophthalmology were analyzed, as AI will be part of the decision-making regarding the scientific investigation, diagnosis and therapeutic management.
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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.