Artificial Intelligence in Myopia: Detection, Prediction, and Future Challenges

As myopia surges toward becoming a global health crisis—with projections suggesting nearly half the world’s population will be affected by 2050—researchers are turning to artificial intelligence (AI) as a critical tool in the early detection, risk assessment, and long-term management of this pervasive condition. A recent review published in Pediatric Investigation explores the expanding role of AI in myopia care, offering a comprehensive overview of how emerging technologies could transform clinical practice and public health outcomes.

Myopia, or nearsightedness, currently impacts more than two billion individuals worldwide. When left uncorrected, it can impair vision, hinder educational and occupational prospects, and significantly reduce quality of life. High myopia, in particular, raises the risk of sight-threatening complications such as retinal detachment, macular degeneration, and glaucoma—outcomes that carry not just personal, but economic and societal burdens.

The review, authored by Drs. Li Li, Jifeng Yu, and Nan Liu from Capital Medical University’s Department of Ophthalmology, outlines a rapidly evolving landscape in which AI is being trained to perform a range of functions traditionally reserved for ophthalmologists. Using machine learning (ML) and deep learning (DL), AI systems can now interpret fundus photographs and optical coherence tomography (OCT) scans to detect early signs of myopia. By analyzing subtle patterns and color variations in retinal images, AI models are learning to diagnose and even predict future cases—capabilities that may soon become indispensable in pediatric eye care.

Beyond static imaging, AI is finding its place in dynamic monitoring tools. Devices like the SVOne, a handheld wavefront sensor, harness AI to detect refractive errors by referencing cloud-based image libraries. Similarly, the Vivior monitor tracks behavioral patterns—such as prolonged near work in children aged 6 to 16—using machine learning algorithms to flag habits linked to myopia onset. These tools underscore AI’s potential not just in clinical diagnostics, but in real-world, day-to-day monitoring that supports early intervention.

On the research front, AI is also revolutionizing risk stratification. Models built using methods like support vector machines, logistic regression, and XGBoost can process extensive longitudinal data to isolate the environmental, genetic, and physiological risk factors most strongly linked to myopia. 

Perhaps most impactful is AI’s capacity to forecast disease progression. By training on biometric data, refractive trends, and treatment outcomes from diverse patient populations, predictive models can help clinicians refine management plans for individual patients. When deployed at scale, these insights could also inform public health policy and clinical guidelines for myopia control.

Yet as promising as AI appears, the review is clear-eyed about its limitations. Training data must be accurate and diverse—bias, poor quality inputs, or datasets drawn predominantly from tertiary care centers may distort real-world applicability. Moreover, most AI models cannot yet provide a transparent clinical rationale for their outputs, limiting their acceptance among practitioners. Privacy and data protection also remain ongoing concerns, particularly given the sensitive nature of medical imaging and patient histories.

As the prevalence of myopia accelerates worldwide, the urgency for scalable, accurate, and accessible solutions becomes increasingly apparent. AI—armed with the ability to learn, predict, and monitor—offers a promising frontier in this battle. But for now, its success will depend as much on careful development and integration as on its raw computational power.