ReviewFrontiers in public health2022
An overview of artificial intelligence in diabetic retinopathy and other ocular diseases.
Review in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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.
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Who cites it
32 citing papers in PubMed.
- A hybrid model for early diagnosis of ophthalmology diseases leveraging CNNs, SBOA optimization, and XAI for visualization.Physical and engineering sciences in medicine · 2026Article
- Code-free automated machine learning for OCT-based classification of vitreoretinal interface diseases.International journal of retina and vitreous · 2026Article
- Deep Learning Model to Detect Diabetic Retinopathy in 45° Images Using Ground Truth from Ultra-Widefield Imaging.Ophthalmology science · 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
- Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.Cureus · 2025Review
- Leveraging XGBoost and explainable AI for accurate prediction of type 2 diabetes.BMC public health · 2025Article
- Artificial Intelligence in Ophthalmology: Acceptance, Clinical Integration, and Educational Needs in Switzerland.Journal of clinical medicine · 2025Article
- From Pathophysiology to Innovative Therapies in Eye Diseases: A Brief Overview.International journal of molecular sciences · 2025Review
- Artificial intelligence in managing retinal disease-current concepts and relevant aspects for health care providers.Wiener medizinische Wochenschrift (1946) · 2025Review
- Perceptions and Earliest Experiences of Medical Students and Faculty With ChatGPT in Medical Education: Qualitative Study.JMIR medical education · 2025Article
- Research on grading detection methods for diabetic retinopathy based on deep learning.Pakistan journal of medical sciences · 2025Article
- Advances in Controlled Release Formulations for Ocular Diseases: Improving Patient Compliance and Therapeutic Outcomes.Current drug metabolism · 2025Review
- AI-Assisted Screening for Diabetic Retinopathy and Fundus Abnormalities in a Large-Scale Physical Examination Population.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Carnosic acid attenuates diabetic retinopathy via the SIRT1 signaling pathway: neuroprotection and endothelial cell preservation.American journal of translational research · 2025Article
- Diabetes and Cataracts Development-Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study.Medicina (Kaunas, Lithuania) · 2024Article
- Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Review
- Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.Journal of personalized medicine · 2024Review
- Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.Eye and vision (London, England) · 2024Review
- Chitosan as a promising materials for the construction of nanocarriers for diabetic retinopathy: an updated review.Journal of biological engineering · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.
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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 Socratic graph.