Application of artificial intelligence for automated interpretation of optical retinal images in diabetic retinopathy

This article explores the application of artificial intelligence (AI) for the automated interpretation of optical retinal images in diabetic retinopathy. It presents the main imaging methods, including fundus photography and optical coherence tomography, and analyzes deep learning algorithms used to detect retinopathic changes. The study evaluates the effectiveness of current autonomous systems, such as IDx-DR and EyeArt, and outlines key limitations of their use. Special attention is given to ethical, technical, and legal aspects of AI implementation in ophthalmic practice. The article highlights AI’s potential as a tool for early screening and prevention of vision loss in diabetic patients.