ReviewFrontiers in endocrinology2022
Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy.
Review in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.
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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
47 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types.Cancer medicine · 2025Pooled it
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Artificial intelligence in geriatric healthcare: a scoping review.BMC geriatrics · 2026Article
- Diagnostic performance of an artificial intelligence software for diabetic retinopathy organised screening in a pilot study.Scientific reports · 2026Article
- [Diagnostic accuracy of tandem mass spectrometry combined with artificial intelligence for neonatal methylmalonic acidemia and associated factors].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Article
- Application of artificial intelligence in lung cancer diagnosis, therapy, and prognosis.BMC medical genomics · 2026Review
- A fundus image dataset for intelligent diabetic retinopathy system.Scientific data · 2026Article
- Diagnostic Accuracy and Real-Life Advantages of the MONA.health Artificial Intelligence Software in Screening for Diabetic Retinopathy and Maculopathy.Diagnostics (Basel, Switzerland) · 2026Article
- Classification of Choroidal Neovascularization and Diabetic Macular Edema Based on Feature Extraction from Optical Coherence Tomography Images.Advances in experimental medicine and biology · 2026Article
- Development and Novel Therapeutics in Diabetic Retinopathy.Current diabetes reviews · 2026Review
- A machine learning model for diabetic retinopathy risk stratification using routine blood and urine parameters: insights into kidney-eye crosstalk.Frontiers in endocrinology · 2026Article
- Autonomous Artificial Intelligence in Diabetic Retinopathy Testing-Lessons Learned on Successful Health System Adoption.Ophthalmology science · 2026Article
- Novel Therapeutic Strategies for Obesity-Related Glomerulopathy.Current hypertension reports · 2025Review
- Evaluation of the degree of agreement in the diagnosis of diabetic retinopathy between ophthalmologists and EyeArtInternational journal of retina and vitreous · 2025Article
- Ocular toxicities associated with MEK/BRAF inhibitors: assessing the accuracy and completeness of large language models.Eye (London, England) · 2025Article
- Precision Medicine for Diabetic Retinopathy: Integrating Genetics, Biomarkers, Lifestyle, and AI.Genes · 2025Review
- Article
- Diagnostic Performance of ChatGPT-4o in Analyzing Oral Mucosal Lesions: A Comparative Study with Experts.Medicina (Kaunas, Lithuania) · 2025Article
- The Role of Artificial Intelligence in the Diagnosis and Management of Diabetic Retinopathy.Journal of clinical medicine · 2025Review
- Artificial intelligence for advancing eye care in resource-poor settings: Assessing the predictive accuracy of an AI-model for diabetic retinopathy screening in India.Global epidemiology · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning evolves into a new form of machine learning technology that is classified under artificial intelligence (AI), which has substantial potential for large-scale healthcare screening and may allow the determination of the most appropriate specific treatment for individual patients. Recent developments in diagnostic technologies facilitated studies on retinal conditions and ocular disease in metabolism and endocrinology. Globally, diabetic retinopathy (DR) is regarded as a major cause of vision loss. Deep learning systems are effective and accurate in the detection of DR from digital fundus photographs or optical coherence tomography. Thus, using AI techniques, systems with high accuracy and efficiency can be developed for diagnosing and screening DR at an early stage and without the resources that are only accessible in special clinics. Deep learning enables early diagnosis with high specificity and sensitivity, which makes decisions based on minimally handcrafted features paving the way for personalized DR progression real-time monitoring and in-time ophthalmic or endocrine therapies. This review will discuss cutting-edge AI algorithms, the automated detecting systems of DR stage grading and feature segmentation, the prediction of DR outcomes and therapeutics, and the ophthalmic indications of other systemic diseases revealed by AI.
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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 OpenQuestion graph.