Novel artificial intelligence applications for pediatric retina.

Abstract

Purpose of review: This review examines the emerging role of artificial intelligence (AI) in the detection and management of pediatric retinal diseases, with a focus on systems that achieve expert-level performance in classifying fundus abnormalities. We highlight models developed for clinical application as assistive or autonomous tools with the potential to enhance early identification and referral, streamline care delivery, and improve access to care.

Recent findings: AI systems, have shown high diagnostic accuracy in identifying retinal pathology associated with retinopathy of prematurity, pediatric myopia, diabetic retinopathy, and retinoblastoma. Many of these systems have potential utility in real-world screening, supplementing clinical decision-making, and guiding early intervention. In addition, autonomous AI systems can increase access in low-resource, remote settings and areas where access to pediatric ophthalmologists is limited. Ongoing advances include integration with nonmydriatic fundus photography, smartphone-based imaging, and image-free diagnostic modalities, further expanding reach and applicability.

Summary: AI holds transformative promise for pediatric retina care by providing scalable, accurate, and accessible screening solutions. These systems have demonstrated to enhance clinical expertise, minimize interobserver variability, facilitate timely referrals and support decision-making. As integration of algorithms into clinical and community settings is established, AI is poised to become an essential component of pediatric ophthalmology, improving early detection and reducing the global burden of preventable childhood blindness.