The concept of Artificial Intelligence (AI) has evolved significantly over the past 70 years, driven by advancements in computational power, data availability, and algorithmic innovations. Today, AI permeates daily life through applications like autonomous vehicles and chatbots, with healthcare emerging as a particularly promising field for its application. In ophthalmology, ancillary imaging exams, such as retinal fundus photos and optical coherence tomography, have become vital tools for screening and diagnosing various eye diseases. The development of AI-driven systems, particularly in specialties such as diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity, offers the potential to transform patient care by improving diagnostic accuracy, early detection, and disease management. This chapter explores the advancements of AI in the retinal field, focusing on the role of machine learning and deep learning algorithms in disease classification and outcome prediction. AI systems like LumineticsCore, EyeArt, and AEYE-DS have garnered regulatory approvals, highlighting their potential in clinical practice. The chapter also explores emerging fields, such as the use of AI for systemic disease associations through ophthalmological exams and the potential of language models like GPT-4 in healthcare. However, despite the considerable promise, challenges remain. These include biases in algorithm development, economic barriers, the need for robust IT infrastructure, and a gap between algorithm development and clinical application. The chapter concludes by discussing future directions for AI in retinal care, emphasizing the need for real-world integration, continuous recalibration, and prospective studies to assess AI’s true impact on clinical outcomes.