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Despite evidence that half the world's population will be myopic by 2050, conventional approaches---school-based vision screening (Phase 1.0) and evidence-based risk factor management (Phase 2.0)---have proven insufficient. We review the emergence of Myopia Prevention and Control 3.0, defined by AI integration across three interconnected domains forming a closed-loop pipeline: (1) AI-driven risk stratification predicting individual-level risk through machine learning on multimodal data; (2) AI-enabled proactive monitoring via wearables, smartphones, and school screening networks; and (3) AI-powered personalized intervention with closed-loop feedback. We critically evaluate evidence across each stage","title":"Myopia Prevention and Control 3.0: Artificial Intelligence--Driven Risk Stratification, Proactive Monitoring, and Personalized Intervention","url":"https://arxiv.org/abs/2607.24187","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.24187v1 Announce Type: new \nAbstract: The convergence of artificial intelligence (AI), digital sensing, and ubiquitous computing has created an unprecedented opportunity to transform myopia prevention from a reactive, population-based model into a proactive, precision-driven one. Despite evidence that half the world's population will be myopic by 2050, conventional approaches---school-based vision screening (Phase 1.0) and evidence-based risk factor management (Phase 2.0)---have proven insufficient. We review the emergence of Myopia Prevention and Control 3.0, defined by AI integration across three interconnected domains forming a closed-loop pipeline: (1) AI-driven risk stratification predicting individual-level risk through machine learning on multimodal data; (2) AI-enabled proactive monitoring via wearables, smartphones, and school screening networks; and (3) AI-powered personalized intervention with closed-loop feedback. 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