Ophthalmic therapeutics faces persistent challenges in delivering
individualized treatment strategies, predicting therapeutic outcomes, and optimizing
long-term patient management across diverse disease profiles. Traditional treatment
paradigms often rely on generalized clinical guidelines, which may not adequately
account for patient-specific variations in disease progression, comorbidities, and
therapeutic response. To address these limitations, this chapter introduces an artificial
intelligence (AI)-driven framework to enhance precision in ophthalmic therapeutics by
facilitating data-informed, personalized, and predictive care. It introduces key AI
methodologies—including deep learning, federated learning, and knowledge graphenhanced modeling—and explores their application in the treatment of ophthalmic
conditions, including age-related macular degeneration, diabetic retinopathy, and
glaucoma. The chapter demonstrates how AI models contribute to individualized
treatment planning, dosage optimization, and therapeutic outcome forecasting, thereby
improving clinical efficacy and patient-centered care. In addition, the chapter
introduces the integration of AI with Traditional Chinese Medicine (TCM) practices in
ophthalmology, offering evidence-based evaluations of acupuncture, herbal therapies,
and multimodal treatment protocols through AI-supported validation frameworks.
Real-world case studies are presented to highlight AI-assisted innovations in
ophthalmic surgery, stem cell therapy, and longitudinal patient monitoring. These case
studies illustrate how AI enhances both mechanistic understanding and clinical
outcomes. Moreover, the chapter critically examines the ethical, regulatory, and
operational challenges involved in deploying AI in therapeutic contexts, including data
privacy, model interpretability, and the standardization of AI applications in diverse
clinical environments. By bridging algorithmic innovation with therapeutic practice,
this chapter provides a comprehensive foundation for researchers, clinicians, and
healthcare stakeholders aiming to advance the next generation of ophthalmic care. It
underscores the transformative potential of AI in redefining ophthalmic therapeutics
and outlines a strategic roadmap for responsible, scalable, and patient-specific AI
integration in the evolving landscape of precision medicine.
Keywords: Artificial intelligence, AI in glaucoma, AI in diabetic retinopathy, AI in age-related macular degeneration, AI-assisted therapeutics, AI-driven surgery, AI in ocular surface disease, Clinical decision support systems, Computational ophthalmology, Digital health, Deep learning, Explainable AI, Knowledge graphs, Machine learning, Ophthalmology, Predictive analytics, Personalized medicine, Retinal disease treatment, Stem cell therapy, Traditional chinese medicine.
