发表机构
Shanghai Nile Intelligent Technology Co., Ltd.; Beijing Tanyuan Academy of Intelligent Sensing（上海尼罗智能科技有限公司; 北京潭渊智能传感研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究借助人工智能、数字传感等技术推动近视防控从被动变主动精准模式。通过多模态数据机器学习预测风险、可穿戴等监测及个性化干预形成闭环。评估各阶段证据，讨论相关挑战并给出未来方向。
AI 中文摘要
人工智能、数字传感和普适计算的融合为将近视防控从基于人群的被动模式转变为主动、精准驱动的模式创造了前所未有的机会。尽管有证据表明到2050年全球一半人口将近视，但传统方法（基于学校的视力筛查和基于证据的风险因素管理）已被证明不足。我们回顾了近视防控3.0的出现，它由跨三个相互关联领域的人工智能集成定义，形成一个闭环管道：（1）通过对多模态数据进行机器学习预测个体水平风险的人工智能驱动风险分层；（2）通过可穿戴设备、智能手机和学校筛查网络进行的人工智能主动监测；（3）具有闭环反馈的人工智能个性化干预。我们批判性地评估了每个阶段的证据，讨论了数据质量、模型验证、伦理和公平性方面的挑战，并概述了未来方向，包括多模态基础模型、数字双胞胎和因果机器学习。
英文摘要
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. We critically evaluate evidence across each stage, discuss challenges in data quality, model validation, ethics, and equity, and outline future directions including multimodal foundation models, digital twins, and causal machine learning.
