[Artificial intelligence in pediatric ophthalmology: applications and perspectives]

Pediatric visual health is not only inextricably linked to an individual's lifelong cognitive development and quality of life, but also constitutes a major public health issue concerning the quality of the national population and the long-term development of the nation. However, the early identification and timely intervention of abnormal visual development in children are severely constrained by multiple factors: the shortage of specialized pediatric ophthalmologists, limited screening capacity in primary healthcare settings, and the highly insidious onset of most childhood ocular diseases. Against this backdrop, the widespread adoption of artificial intelligence (AI) technology has emerged as a core strategic pathway to improve screening coverage, ensure the standardization and consistency of diagnosis and treatment, and deliver inclusive pediatric eye health services. Nevertheless, the clinical application of AI in pediatric ophthalmology faces multiple challenges, including the scarcity of high-quality data on pediatric visual development, insufficient model robustness, limited generalization ability, the lack of multi-center clinical validation, and ethical and regulatory frameworks that lag behind technological innovation. This paper systematically summarizes the core bottlenecks and challenges confronting the promotion and application of AI technology in the field of pediatric ophthalmology, provides an in-depth analysis of its application prospects in childhood ocular disease screening, precision diagnosis and treatment, and full-cycle health management, and puts forward a strategic vision for building an open and collaborative national intelligent platform for pediatric eye health. This work aims to advance the deep integration of AI technology with standardized clinical practice in pediatric ophthalmology, and ultimately establish a new full-cycle, intelligent, and inclusive service system for pediatric eye health.