Myopia Prevention and Control 3.0: Artificial Intelligence--Driven Risk Stratification, Proactive Monitoring, and Personalized Intervention
2026-07-27 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors explain how new technologies like AI and digital devices can change how we prevent nearsightedness (myopia). Traditional methods like school vision screenings are not enough to stop the rise in myopia cases worldwide. They describe a new approach called Myopia Prevention and Control 3.0, which uses AI to predict who is at risk, monitor eye health with gadgets, and provide personalized treatments. The authors also highlight challenges with data, fairness, and ethics, and suggest future improvements using advanced AI techniques.
myopiaartificial intelligencemachine learningrisk stratificationwearablesdigital sensingpersonalized interventionclosed-loop feedbackmodel validationdigital twins
Authors
Tieniu Wang, Cangzhu Huang, Qianhui Li
Abstract
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.