ReviewJournal of ophthalmic & vision research
Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review.
Review in Journal of ophthalmic & vision research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed.
- Research on the application of computer-assisted surgical technology in ophthalmic plastic surgery education.BMC medical education · 2025Trial
- A Review of the Use of Artificial Intelligence in Ophthalmology Imaging: Approximation to Ocular Histopathology.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2026Review
- Machine Learning-Based Prediction of Strabismus with and without Amblyopia from Fixation Eye Movements.Ophthalmology science · 2026Article
- Moral diversity and the challenge of responsibility in AI-CDSS.Philosophy, ethics, and humanities in medicine : PEHM · 2026Article
- The Irreducible Encounter: Human Skills, Artificial Intelligence, and the Future of Ophthalmic Care.Ophthalmology and therapy · 2026Article
- Emerging innovations in ophthalmic drug delivery for diabetic retinopathy: a translational perspective.Drug delivery and translational research · 2026Review
- Surgical capacity in ophthalmology: the unmet need for sustainable solutions.Eye (London, England) · 2026Review
- Artificial intelligence in ophthalmology: from innovation to clinical integration.Frontiers in ophthalmology · 2026Article
- Awareness, trust, and expectations of AI for glaucoma care among Bulgarian ophthalmologists: Role of demographic factors.PLOS digital health · 2026Article
- Global perspectives of ophthalmologists on artificial intelligence adoption in clinical practice.International journal of retina and vitreous · 2025Article
- Exploring AI's Potential in Papilledema Diagnosis to Support Dermatological Treatment Decisions in Rural Healthcare.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Ophthalmology: Acceptance, Clinical Integration, and Educational Needs in Switzerland.Journal of clinical medicine · 2025Article
- Lights and Shadows on Artificial Intelligence in Glaucoma: Transforming Screening, Monitoring, and Prognosis.Journal of clinical medicine · 2025Review
- Comparing No-Code Platforms and Deep Learning Models for Glaucoma Detection From Fundus Images.Cureus · 2025Article
- Prediction of the Cause of Fundus-Obscuring Vitreous Hemorrhage Using Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Strategies for Early Keratoconus Diagnosis: A Narrative Review of Evaluating Affordable and Effective Detection Techniques.Journal of clinical medicine · 2025Review
- Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program [Letter].Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- A comparative study of GPT-4o and human ophthalmologists in glaucoma diagnosis.Scientific reports · 2024Observational
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
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) holds immense promise for transforming ophthalmic care through automated screening, precision diagnostics, and optimized treatment planning. This paper reviews recent advances and challenges in applying AI techniques such as machine learning and deep learning to major eye diseases. In diabetic retinopathy, AI algorithms analyze retinal images to accurately identify lesions, which helps clinicians in ophthalmology practice. Systems like IDx-DR (IDx Technologies Inc, USA) are FDA-approved for autonomous detection of referable diabetic retinopathy. For glaucoma, deep learning models assess optic nerve head morphology in fundus photographs to detect damage. In age-related macular degeneration, AI can quantify drusen and diagnose disease severity from both color fundus and optical coherence tomography images. AI has also been used in screening for retinopathy of prematurity, keratoconus, and dry eye disease. Beyond screening, AI can aid treatment decisions by forecasting disease progression and anti-VEGF response. However, potential limitations such as the quality and diversity of training data, lack of rigorous clinical validation, and challenges in regulatory approval and clinician trust must be addressed for the widespread adoption of AI. Two other significant hurdles include the integration of AI into existing clinical workflows and ensuring transparency in AI decision-making processes. With continued research to address these limitations, AI promises to enable earlier diagnosis, optimized resource allocation, personalized treatment, and improved patient outcomes. Besides, synergistic human-AI systems could set a new standard for evidence-based, precise ophthalmic care.
Indexed as
Identifiers
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.