A Survey on AI-Powered Ophthalmology: A Revolution in Eye Care and Disease Management

Artificial Intelligence (AI) is transforming ophthalmology by enhancing diagnostic accuracy, optimizing treatment strategies, and expanding access to quality eye care. AI-powered machine learning and deep learning models analyze vast ophthalmic datasets, enabling the early detection of diseases such as glaucoma, diabetic retinopathy, cataracts and age-related macular degeneration (AMD). These technologies improve image interpretation from fundus photography and optical coherence tomography, providing ophthalmologists with precise assessments of retinal health and disease progression. Beyond diagnostics, AI contributes to ophthalmic surgery by increasing precision in robotic-assisted procedures and enhancing preoperative planning. AI-driven teleophthalmology further reduces healthcare gaps by enabling remote screenings and consultations, particularly in low-access regions. Furthermore, AI-based automation improves clinical workflows, reducing administrative burdens and optimizing resource allocation. Despite its advantages, AI adoption in ophthalmology faces challenges, including data privacy concerns, algorithmic bias, and the need for regulatory approval. For widespread implementation, it is fundamental to ensure diverse, high-quality datasets and achieve seamless integration with existing healthcare systems. This study explores AI-driven advancements in ophthalmology, highlighting current applications, benefits, limitations, and future research directions. By leveraging AI, the field of ophthalmology is poised for a paradigm shift, improving patient outcomes and revolutionizing eye care globally.