Myopia Prevention and Control 3.0: Artificial Intelligence–Driven Risk Stratification, Proactive Monitoring, and Personalized Intervention

Myopia prevention has historically progressed through two phases, both of which are increasingly inadequate for the global myopia epidemic. Phase 1.0, the traditional school-based vision screening model, is inherently reactive, identifying myopia only after it has already developed. Phase 2.0, which focuses on evidence-based risk factor management (such as increasing outdoor time and using low-dose atropine), is limited by its population-level approach, which fails to account for individual genetic and environmental heterogeneity or provide a mechanism for continuous, adaptive care.

The authors propose a third paradigm, Myopia Prevention and Control 3.0, which leverages artificial intelligence (AI), digital sensing, and ubiquitous computing to create a continuous, closed-loop pipeline. This framework consists of three interconnected stages:

1. AI-Driven Risk Stratification: Uses machine learning to integrate multimodal data—including fundus imaging, clinical history, and behavioral patterns—to predict individual-level risk of myopia onset and progression.
2. AI-Enabled Proactive Monitoring: Employs wearables, smartphones, and school-based networks to continuously track environmental and physiological parameters, detecting early signals of change.
3. AI-Powered Personalized Intervention: Dynamically selects and adjusts treatment strategies based on real-time feedback from the monitoring stage, ensuring that interventions are tailored to the individual's specific response and risk profile.

By moving from static, episodic care to a dynamic, data-driven ecosystem, the 3.0 paradigm aims to transform myopia management into a precision-based field. This shift is critical because it addresses the 'evidence-to-practice' gap, where standardized recommendations often fail due to poor adherence and variable individual responses. The authors argue that this framework provides a necessary blueprint for clinicians and policymakers to move beyond population-wide averages and toward truly personalized, adaptive myopia control.