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June 27, 2026Annals of Optometry and Contact Lens

Artificial Intelligence in Myopia Management: Risk Prediction, Monitoring, and Clinical Decision Support

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Authors

JJJinho JeongJeju National University Hospital

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Overview

Review demonstrates AI improves myopia risk prediction and management, suggesting enhanced clinical outcomes.

Key Points

  • This review aims to evaluate AI applications in myopia management, focusing on predictive risk assessment and clinical decision support.
  • Conducted a comprehensive literature search on PubMed for studies from 2018 to 2025.
  • Reviewed advances in AI, including deep learning systems and federated learning frameworks.
  • Assessed the performance of AI models in detecting myopia and predicting progression.
  • Deep learning systems achieved over 0.95 AUC for pathologic myopia detection.
  • AI-guided interventions reduced high myopia incidence by 17.8% in simulated trials.
  • Vision transformers showed superior performance over traditional models in detecting lesions.

Cite This Study

Jinho Jeong (2026) studied this question.

synapsesocial.com/papers/6a3f6972aea7db3c19540412https://doi.org/10.52725/aocl.2026.25.2.47
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Advancing Artificial Intelligence in Myopia Management: From Predicting Progression to Supporting Clinical Decisions2026
  2. 2Predicting the Onset of Myopia in Children and Adolescents Using Artificial Intelligence: A Systematic Review and Meta-analysis2026
  3. 3Shaping the future of myopia: artificial intelligence for vitreoretinal complications of high and pathologic myopia2026
  4. 4A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children2024 · 55 citations
  5. 5Revolutionizing Pediatric Myopia Care: A Machine Learning Approach for Rapid and Accurate Pre-clinical Screening2026