International Journal of Medical Discoveries

Vision loss and blindness caused by chronic-eye diseases such as diabetic eye disease, glaucoma and age-related macular degeneration are a major cause of irreversible vision loss. Many of these diseases progress silently without symptoms and are often diagnosed only through a thorough visual examination. With technological advancements in artificial intelligence (AI) and miniaturized, wearable sensing devices, there is now an opportunity for continuous, out-of-hospital monitoring, enabling earlier detection and intervention. This article outlines a new conceptual framework for a wearable AIintegrated solution for continuous monitoring of the eye. The framework presents its major components including system architecture, data processing pipelines, and AI inference workflows. We conducted a comprehensive literature review from 2010–2024 using MEDLINE, Embase, and IEEE Xplore, covering multiple publication types in three areas: AI-assisted diagnosis using ophthalmic images, chronic disease monitoring via wearable biosensors, and digital adoption of technology in healthcare. The information obtained allowed us to propose an artificial intelligent framework consisting of four layers: data collection, edge processing, AI inference, and clinical communication. The results support the use of deep learning for analysis of ophthalmic images and provide evidence that wearable biosensors have a role in chronic disease management. However, challenges remain including regulatory, technical, ethical, and equity issues. Strategies such as privacy-preserving AI, adaptive regulatory frameworks, and inclusive system design offer potential solutions. The application of wearable AI for continuous monitoring will evolve as a new paradigm for ophthalmic care, and the proposed framework can support future development and clinical implementation.