Advancing artificial intelligence in myopia management: from predicting progression to supporting clinical decisions
image: The graphical abstract illustrates the transition of myopia care from premyopia to myopia, high myopia, and pathologic myopia. Multimodal data, including demographic and refractive information, genetic, behavioral and environmental factors, ophthalmic imaging, and omics profiles, can be integrated into artificial intelligence–based prediction models to support individualized risk stratification, progression forecasting, and treatment-response prediction. These models may inform intervention selection across myopia control, myopia correction, and pathologic myopia treatment, while requiring consideration of fairness, safety, trustworthiness, and cost-effectiveness. Ultimately, AI-driven decision support may contribute to precision, whole-life myopia management from childhood to adulthood and older age.
Credit: Zewei Zhang
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.
(Expert Contact: zhoujibo1000@aliyun.com)