<p>Artificial intelligence (AI) is progressively advancing across various medical specialties, particularly ophthalmology, to evaluate medical data, prevent diseases, and facilitate individualized treatment strategies. This narrative review investigates the current state of AI applications in ophthalmology clinical trials, focusing on patient monitoring, treatment optimization, and the diagnosis of prevalent ocular disorders. The implementation of AI techniques, such as deep learning and machine learning, improves the precision and efficacy of the process by evaluating ocular imaging data, such as OCTs, to diagnose various conditions, including diabetic retinopathy, age-related macular degeneration, glaucoma, and cataracts. Furthermore, AI has transformed patient recruiting, data management, and outcome assessment in clinical trials. In resource-limited settings, the combination of AI and teleophthalmology resulted in greater access to care. Despite its many positive aspects, we must address challenges like data bias, transparency, and ethical concerns to ensure the safe and equitable implementation of AI. As AI progresses, it has the capability to transform ophthalmic care and research, significantly improving patient outcomes and worldwide healthcare accessibility.</p>