ReviewJournal of ophthalmology2018
Applications of Artificial Intelligence in Ophthalmology: General Overview.
Review in Journal of ophthalmology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis 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
54 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optical coherence tomography angiography in thyroid associated ophthalmopathy: a systematic review.BMC ophthalmology · 2024Pooled it
- A hybrid model for early diagnosis of ophthalmology diseases leveraging CNNs, SBOA optimization, and XAI for visualization.Physical and engineering sciences in medicine · 2026Article
- LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology.ACM transactions on computing for healthcare · 2026Article
- Optical Coherence Tomography Biomarkers Predicting Progression to Atrophy in Non-Exudative Age-Related Macular Degeneration.Diagnostics (Basel, Switzerland) · 2026Review
- Comprehensive Evaluation of ChatGPT's Diagnostic Accuracy on Image-based Ophthalmic Case Interpretations.Ophthalmology science · 2026Article
- Multimodal Diagnostic Accuracy of GPT-4.o, Claude 3.7, and Gemini 2.5 on Real-World Retina Cases.Journal of vitreoretinal diseases · 2026Article
- Investigating the Impact of Semi-Supervised Learning Methods to Improve the Quality of Diagnosis of Retinal Diseases from OCT Images.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning algorithms for timely diagnosis of retinopathy of prematurity requiring treatment.Eye (London, England) · 2026Article
- Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.Cureus · 2025Review
- Revolutionizing AMD detection Bi model CNNs and hybrid feature selection for automated grading.Scientific reports · 2025Article
- The efficacy of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis.International journal of retina and vitreous · 2025Article
- Application of artificial intelligence in Alzheimer's disease: a bibliometric analysis.Frontiers in neuroscience · 2025Review
- Ocular Duction Measurement Using Three Convolutional Neural Network Models: A Comparative Study.Cureus · 2024Article
- Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review.PLOS digital health · 2024Review
- Machine Learning and Optical-Coherence-Tomography-Derived Radiomics Analysis to Predict the Postoperative Anatomical Outcome of Full-Thickness Macular Hole.Bioengineering (Basel, Switzerland) · 2024Article
- Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence as diagnostic modality for keratoconus: A systematic review and meta-analysis.Journal of Taibah University Medical Sciences · 2024Review
- Predicting Clinician Fixations on Glaucoma OCT Reports via CNN-Based Saliency Prediction Methods.IEEE open journal of engineering in medicine and biology · 2024Article
- Supervised Machine Learning Methods for Seasonal Influenza Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Acceptance and Usability of an Innovative mDentistry eHygiene Model Amid the COVID-19 Pandemic Within the US National Dental Practice-Based Research Network: Mixed Methods Study.JMIR human factors · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the emergence of unmanned plane, autonomous vehicles, face recognition, and language processing, the artificial intelligence (AI) has remarkably revolutionized our lifestyle. Recent studies indicate that AI has astounding potential to perform much better than human beings in some tasks, especially in the image recognition field. As the amount of image data in imaging center of ophthalmology is increasing dramatically, analyzing and processing these data is in urgent need. AI has been tried to apply to decipher medical data and has made extraordinary progress in intelligent diagnosis. In this paper, we presented the basic workflow for building an AI model and systematically reviewed applications of AI in the diagnosis of eye diseases. Future work should focus on setting up systematic AI platforms to diagnose general eye diseases based on multimodal data in the real world.
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.