ReviewFrontiers in cell and developmental biology2024
The application of artificial intelligence in diabetic retinopathy: progress and prospects.
Review in Frontiers in cell and developmental biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association of estimated glomerular filtration rate with proliferative diabetic retinopathy: a systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
- Article
- Advances in diagnosing and treating diabetic retinopathy.Nature reviews. Endocrinology · 2026Review
- Active Learning Under Expert-Budget Constraints: A Human-in-the-Loop Pipeline for Diabetic Retinopathy Lesion Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Addressing Unmet Needs in the Management of Diabetes in Asia and Enhancing Diabetes Care Through the Use of Digital Technology: An Expert Opinion.Journal of the ASEAN Federation of Endocrine Societies · 2026Review
- The Central Role of Oxidative Stress in Diabetic Retinopathy: Advances in Pathogenesis, Diagnosis, and Therapy.Diagnostics (Basel, Switzerland) · 2026Review
- Evaluation of the degree of agreement in the diagnosis of diabetic retinopathy between ophthalmologists and EyeArtInternational journal of retina and vitreous · 2025Article
- Review
- Integrating Artificial Intelligence Into Telemedicine: Evidence, Challenges, and Future Directions.Cureus · 2025Review
- A panoramic perspective: application prospects and outlook of multimodal artificial intelligence in the management of diabetic retinopathy.Frontiers in public health · 2025Review
- Deep learning-driven approach for cataract management: towards precise identification and predictive analytics.Frontiers in cell and developmental biology · 2025Review
- Artificial intelligence in cataract grading system: a LOCS III-based hybrid model achieving high-precision classification.Frontiers in cell and developmental biology · 2025Article
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
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
In recent years, artificial intelligence (AI), especially deep learning models, has increasingly been integrated into diagnosing and treating diabetic retinopathy (DR). From delving into the singular realm of ocular fundus photography to the gradual development of proteomics and other molecular approaches, from machine learning (ML) to deep learning (DL), the journey has seen a transition from a binary diagnosis of "presence or absence" to the capability of discerning the progression and severity of DR based on images from various stages of the disease course. Since the FDA approval of IDx-DR in 2018, a plethora of AI models has mushroomed, gradually gaining recognition through a myriad of clinical trials and validations. AI has greatly improved early DR detection, and we're nearing the use of AI in telemedicine to tackle medical resource shortages and health inequities in various areas. This comprehensive review meticulously analyzes the literature and clinical trials of recent years, highlighting key AI models for DR diagnosis and treatment, including their theoretical bases, features, applicability, and addressing current challenges like bias, transparency, and ethics. It also presents a prospective outlook on the future development in this domain.
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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.