(untitled) (Digital Health & Telemonitoring Advances E: 3117-6461 | P: 3117-647X, 2026)
The increasing prevalence of vision disorders such as diabetic retinopathy, glaucoma, and age-related macular degeneration has created a pressing need for scalable and efficient diagnostic solutions. Artificial Intelligence (AI)-enabled ophthalmic telemonitoring has emerged as a transformative approach to address these challenges by facilitating remote retinal image analysis. This paper explores the integration of machine learning and deep learning techniques in teleophthalmology systems for early detection and monitoring of vision disorders. By leveraging retinal fundus images and optical coherence tomography (OCT) data, AI models can identify pathological patterns with high accuracy and efficiency. The study presents a comprehensive framework including data acquisition, preprocessing, feature extraction, and model deployment in remote healthcare settings. Additionally, a case study is discussed to demonstrate the practical implementation of AI-based retinal screening systems. Questionnaire-based analysis further evaluates user acceptance and system usability. The findings indicate that AI-enabled telemonitoring significantly improves early diagnosis, reduces healthcare costs, and enhances accessibility, particularly in rural and underserved regions. However, challenges such as data privacy, model bias, and regulatory constraints must be addressed for large-scale adoption. This research contributes to the advancement of AI-driven ophthalmic diagnostics and highlights future directions for improving telehealth systems.