Advancing Artificial Intelligence in Myopia Management: From Predicting Progression to Supporting Clinical Decisions

Myopia is a major global public health challenge, characterized by excessive axial elongation and an increased risk of irreversible vision‐threatening complications. In clinical practice, management remains experience‐based and is constrained by a limited ability to integrate high‐dimensional non‐linear biometric information, creating an unmet need for more precise and individualized decision‐making. Although artificial intelligence (AI) has demonstrated strong performance in screening, diagnosis, and prediction of myopia onset and progression, most existing studies have focused on forecasting disease trajectories rather than generating actionable support for treatment selection and optimization. In this review, we summarize recent advances in AI applications across the continuum of myopia care, from prediction of onset and progression to early assessment of pathological evolution, and further examine emerging AI‐enabled strategies for intervention planning in orthokeratology, pharmacologic therapy, and refractive surgery, with particular emphasis on treatment responsiveness. Finally, we discuss the major barriers to clinical translation, including data heterogeneity, limited interpretability, and implementation costs, and outline a future framework for an integrated patient‐centered AI ecosystem to support precision management and reduce the global burden of myopia.