AI-Powered Innovations in Ophthalmic Diagnosis and Treatment is a forward-looking assessment of how artificial intelligence is redefining modern eye care. The book connects computational advancements with clinical practice, presenting a structured, multidisciplinary roadmap for integrating AI across diagnostic workflows, therapeutic decisions, patient monitoring, and future-ready developments in ophthalmology. Across seven insightful chapters, the book establishes AI-driven frameworks that enhance ophthalmic workflows through federated, reinforcement, and prompt-based learning. Subsequent chapters detail AI-enabled diagnostics for retinal, glaucoma, and ocular surface diseases, alongside governance challenges in real-world deployment. The book also explores personalized therapeutics by integrating AI with Traditional Chinese Medicine, stem cell strategies, and precision care models. It further examines next-generation technologies including LLMs, AIGC, and multi-agent systems and their transformative roles in clinical research, drug discovery, and medical education. Broader applications are highlighted through AI's connection to systemic disease surveillance and wearable healthcare. The final chapters address regulatory, ethical, and legal considerations, concluding with a visionary outlook on quantum computing and global collaboration shaping the future of ophthalmic AI. Uniting clinical evidence, computational methodologies, and patient-centred perspectives, this book provides actionable insights for implementing responsible, explainable, and scalable AI within ophthalmic practice. Key Features Introduces AI-driven frameworks enhancing diagnostic and clinical workflows in ophthalmology. Presents practical AI applications for retinal, glaucoma, and ocular surface disease detection. Explores AI-guided personalized therapeutics, including TCM integration and stem cell innovations. Examines emerging technologies such as LLMs, AIGC, and multi-agent systems in research and education. Addresses ethical, regulatory, and governance challenges in deploying medical AI. Highlights AI’s role in systemic disease prediction, wearable monitoring, and next-generation healthcare models. Envisions future innovations shaped by quantum computing and global AI collaboration.