AI驱动的近视防控新范式，实现个性化风险评估与干预
Myopia Prevention and Control 3.0: Artificial Intelligence--Driven Risk Stratification, Proactive Monitoring, and Personalized Intervention
- 用多模态数据训练机器学习模型，预测个体近视风险
- 通过可穿戴设备和手机实现主动监测，形成闭环管理
- 适合医疗科技从业者、眼科医生及智能健康研究者
人工智能、数字传感与普适计算的融合，为将近视防控从被动的群体筛查模式转向主动的精准防控模式提供了前所未有的机遇。尽管预计到2050年全球一半人口将患近视，但传统的学校视力筛查（1.0阶段）和基于证据的风险因素管理（2.0阶段）已显不足。本文综述了近视防控3.0的兴起，其核心是人工智能在三个相互关联领域的整合：（1）基于多模态数据的机器学习进行个体风险分层；（2）通过可穿戴设备、智能手机和校园筛查网络实现人工智能驱动的主动监测；（3）具备闭环反馈的个性化干预。我们系统评估各环节证据，讨论数据质量、模型验证、伦理与公平性挑战，并展望未来方向，包括多模态基础模型、数字孪生与因果机器学习。
原文摘要 · Abstract (English)
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.
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