Ocular diseases such as glaucoma, diabetic retinopathy, and age-related macular degeneration are among the leading causes of visual impairment and blindness worldwide, posing a significant global healthcare challenge. Effective management of these conditions is often complicated by the eye’s complex anatomical and physiological barriers, which limit drug penetration and therapeutic efficiency. Conventional ophthalmic formulations frequently experience rapid precorneal drug loss, poor corneal permeability, and low bioavailability, resulting in reduced treatment effectiveness and the need for frequent dosing. Artificial intelligence (AI) is increasingly transforming healthcare by enabling advanced data analysis, predictive modeling, and improved clinical decision-making. In ophthalmology, AI technologies such as machine learning and deep learning are being applied to enhance disease diagnosis, support drug development, and optimize therapeutic strategies. The integration of AI with modern ocular drug delivery systems is emerging as a promising approach to improve treatment precision and patient outcomes. Recent advances in AI also support the development of advanced ophthalmic therapies. AI-based models can predict drug behavior, optimize formulation parameters, and improve targeted drug delivery. In addition, AI is being integrated with emerging approaches such as gene correction, optogenetics, biomarker analysis, and personalized medicine. AI-driven diagnostic and screening tools further enable earlier detection and monitoring of retinal diseases, facilitating timely and more effective treatment. This review highlights the role of advanced AI tools in ocular drug development and therapeutic innovation.
Shanthi Priya R.1, Senthil Prabhu R.*2, Umamaheswari D.2, Selva Kumari N.1, Sherley Rozzario J.1, Venkatarathinakumar T.3 (Wed,) studied this question.