Abstract
Purpose of review
Artificial intelligence (AI) has emerged as a promising tool across multiple stages of myopia care, including screening, risk prediction and treatment planning. However, most published studies remain focused on algorithm development and validation, with considerably less attention directed towards implementation, regulation and real-world deployment. This review examines the current state of AI implementation in myopia and discusses the challenges that must be addressed before successful clinical adoption can occur.
Recent findings
Recent studies have applied AI across multiple stages of the myopia care pathway, from population screening and risk stratification to personalized intervention planning. Nevertheless, evidence regarding real-world deployment remains limited. A small number of implementation studies have begun evaluating integration within existing healthcare systems, though few studies focus on actual patient outcomes and change in management, beyond algorithmic performance alone. Early economic evaluations suggest that AI-enabled programmes may be cost-effective through improved resource allocation and prevention of long-term visual morbidity. No myopia-specific AI system has yet achieved routine clinical implementation as a regulated medical device.
Summary
The principal challenges facing AI in myopia care are implementation, regulation, economics and clinician trust. Future progress will depend not only on algorithmic performance but also on prospective implementation studies, demonstrable economic value and robust governance frameworks. Successful translation will ultimately require demonstration of clinical value within real-world healthcare systems.