This chapter provides a comprehensive overview of experimental artificial intelligence (AI) systems in ophthalmology, emphasizing their applications in diagnostics, disease classification, and clinical workflow optimization. Highlighted areas include keratoconus detection, refractive surgery screening, and automated imaging analysis, with advanced techniques such as convolutional neural networks (CNNs), multi-view data integration, and generative models showing promising results. The chapter also explores the potential of reinforcement learning in surgical simulations, the integration of molecular and genomic data, and AI's transformative role in telemedicine. Despite progress, challenges such as model generalization across clinical settings and real-world validation remain critical for broader clinical adoption.