Diabetic retinopathy represents a significant challenge in global eye care, particularly because early disease may progress without noticeable visual symptoms. Artificial intelligence has emerged as a promising approach for supporting large-scale retinal screening by analyzing digital fundus images and identifying features associated with diabetic retinal changes. AI-assisted screening technologies may contribute to earlier detection and appropriate clinical referral.
This session will explore developments in automated diabetic retinopathy detection, disease severity classification, image-quality assessment, and referral support. Researchers and clinicians will discuss the performance of AI-based screening systems and their potential integration into primary care, community screening programs, ophthalmology services, and digitally enabled healthcare environments.
The session will also examine important considerations surrounding clinical validation, population diversity, algorithmic reliability, regulatory requirements, and patient safety. Discussions will emphasize the importance of combining technological capabilities with appropriate clinical oversight to develop sustainable and evidence-based approaches to diabetic eye disease screening.