ReviewInternational journal of ophthalmology2018
Application of artificial intelligence in ophthalmology.
Review in International journal of ophthalmology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 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
52 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in the diagnosis and prognosis of ocular trauma: a systematic review.BMC ophthalmology · 2026Pooled it
- Describing the Performance and the Infrastructure Requirements of the Existing Artificial Intelligence (AI)-Based Diabetic Retinopathy (DR) Screening Algorithms for Diabetic Patients: an Umbrella Review.Journal of medical systems · 2025Pooled it
- Optical coherence tomography angiography in thyroid associated ophthalmopathy: a systematic review.BMC ophthalmology · 2024Pooled 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
- Computational pathology-based artificial intelligence platform for the identification of common oral potentially malignant disorders.BMC oral health · 2026Article
- Artificial intelligence in ophthalmology clinical trials: a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Posterior segment imaging clues to systemic malignancy: a phenotype-to-confirmation narrative review.Frontiers in ophthalmology · 2026Review
- Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.Cureus · 2025Review
- Deep learning algorithms for identifying developmental dysplasia of the hip based on sonographic images: a retrospective, prospective, multicenter study in China.EClinicalMedicine · 2025Article
- Artificial intelligence applications in refractive error management: A systematic review and meta-analysis.PLOS digital health · 2025Article
- Optical Coherence Tomography Angiography (OCTA) Characteristics of Acute Retinal Arterial Occlusion: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only.Scientific reports · 2025Article
- Deep Learning-Based Detection of Ocular Surface Squamous Neoplasia from Ocular Surface Images.Ocular oncology and pathology · 2025Article
- Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis.Health science reports · 2025Article
- Accuracy of Artificial Intelligence Versus Clinicians in Real-Life Case Scenarios of Retinopathy of Prematurity.Cureus · 2025Article
- Assessing pediatric visual acuity with a computerized optokinetic nystagmus analyzer.Clinics (Sao Paulo, Brazil) · 2025Article
- Comparing Ophthalmologist and Artificial Intelligence Chatbot Responses to Patient Questions.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Impact of Artificial Intelligence on the Knowledge, Attitude, and Performance of Ophthalmology Residents: A Systematic Review.Journal of ophthalmic & vision research · 2025Article
- Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review.PLOS digital health · 2024Review
- Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of this new technology, artificial intelligence has been one of the most influential information technology revolutions. We searched these English-language studies relative to ophthalmology published on PubMed and Springer databases. The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. According to the above studies, we conclude that the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7%, for non-proliferative diabetic retinopathy ranged from 75% to 94.7%, for age-related macular degeneration it ranged from 75% to 100%, for retinopathy of prematurity ranged over 95%, for retinal vein occlusion just one study reported ranged over 97%, for glaucoma ranged 63.7% to 93.1%, and for cataract it achieved a more than 70% similarity against clinical grading.
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.