ReviewAnnals of translational medicine2020
Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization.
Review in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis 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
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis.Journal of medical Internet research · 2021Pooled it
- LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology.ACM transactions on computing for healthcare · 2026Article
- Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.Cureus · 2025Review
- Current status and solutions for AI ethics in ophthalmology: a bibliometric analysis.NPJ digital medicine · 2025Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Detection of Ocular Surface Squamous Neoplasia Using Artificial Intelligence With Anterior Segment Optical Coherence Tomography.American journal of ophthalmology · 2025Article
- Conjunctival Bulbar Redness Extraction Pipeline for High-Resolution Ocular Surface Photography.Translational vision science & technology · 2025Article
- SLID: a slit-lamp image dataset for deep learning-based anterior eye anatomical segmentation and multi-lesion detection.Frontiers in digital health · 2025Article
- Artificial intelligence for posterior capsule opacification.Frontiers in medicine · 2025Review
- Tools and methods for cataract recognition in low-resource settings: A narrative review.PLOS global public health · 2025Review
- Narrative Review of Artificial Intelligence in Ophthalmic Disease Detection : Artificial Intelligence in Ophthalmic Diseases Detection.Galen medical journal · 2025Review
- Establishment of a corneal ulcer prognostic model based on machine learning.Scientific reports · 2024Article
- Review
- Non-invasive and objective tear film breakup detection on interference color images using convolutional neural networks.PloS one · 2023Article
- Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023).International journal of ophthalmology · 2023Article
- Evaluation of a novel deep learning based screening system for pathologic myopia.International journal of ophthalmology · 2023Article
- Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2022Review
- Artificial intelligence and corneal diseases.Current opinion in ophthalmology · 2022Review
- DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.Ophthalmology · 2022Article
- Artificial intelligence in ophthalmology - Machines think!Indian journal of ophthalmology · 2022Article
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) based on machine learning (ML) and deep learning (DL) techniques has gained tremendous global interest in this era. Recent studies have demonstrated the potential of AI systems to provide improved capability in various tasks, especially in image recognition field. As an image-centric subspecialty, ophthalmology has become one of the frontiers of AI research. Trained on optical coherence tomography, slit-lamp images and even ordinary eye images, AI can achieve robust performance in the detection of glaucoma, corneal arcus and cataracts. Moreover, AI models based on other forms of data also performed satisfactorily. Nevertheless, several challenges with AI application in ophthalmology have also arisen, including standardization of data sets, validation and applicability of AI models, and ethical issues. In this review, we provided a summary of the state-of-the-art AI application in anterior segment ophthalmic diseases, potential challenges in clinical implementation and our prospects.
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.