Artificial Intelligence in Screening and Grading Diabetic Eye Diseases: A Systematic Review From Algorithms to Clinic
Authors' conclusion
Artificial intelligence holds significant clinical value and application potential in the graded diagnosis of diabetes-related ocular diseases, improving screening efficiency and consistency, particularly in resource-limited settings. Future efforts should focus on enhancing algorithmic adaptability in complex scenarios, promoting deeper integration of technology with clinical workflows, and establishing robust data security and validation standards to facilitate large-scale and high-quality clinical implementation.
Study context
- Population
- Publications on AI in grading diagnosis of diabetes-related ocular diseases (e.g. DR, DME)
- Participants
- 74 core publications
- Comparison
- AI vs traditional manual diagnosis for grading diabetic eye diseases
Methodological detailsPRISMA guidelines, English-language articles published 2021–2025
- PRISMA guidelines
- English-language articles published 2021–2025
Pooled result
As reported in the abstract
- AI system mainly based on supervised learning with integrated algorithms; significant advantages over traditional manual diagnosis in screening efficiency, diagnostic consistency and healthcare accessibility
- Limitations: identification of early-stage lesions, diagnosis in multidisease comorbidity and cross-device generalisation
- Optimisation strategies: data augmentation with generative adversarial networks, multimodal fusion, enhanced interpretability
- Portable-device screening models have emerged; challenges persist in data security and standardised validation
Original publication
Artificial Intelligence in Screening and Grading Diabetic Eye Diseases: A Systematic Review From Algorithms to Clinic. Diabetes Obes Metab. Published 8 March 2026. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →