Artificial Intelligence in Pediatric Ultrasound: An Update and Future Applications

Abstract

The emerging application of artificial intelligence (AI) in pediatric ultrasound has shown significant potential to improve diagnostic accuracy and efficiency, particularly in addressing the challenges of conventional ultrasound in operator dependence, inconsistent image quality, and limited quantitative analysis capabilities. These limitations arise from the inherent complexity of pediatric ultrasound image interpretation, such as organ immaturity, motion artifacts, and intestinal gas interference. AI can enhance structural recognition, offering automated, standardized measurements. AI applications can also assist non-expert physicians in enhancing diagnostic accuracy. This review summarizes recent advances in AI applications for pediatric ultrasound across different systems, including preliminary diagnosis, screening, detailed analysis, and decision support, while providing a detailed discussion of technical advances, unmet challenges, and future directions. Future research can focus on intelligent cross-system feature analysis frameworks, translational application of AI-driven pediatric ultrasound in multi-disease diagnosis, and fine-tuned models for personalized treatment based on large-scale randomized controlled trials. This review provides an up-to-date reference for clinicians, ultrasound technicians, researchers, and biomedical engineers.

Keywords

Artificial intelligence / decision support / diagnosis / pediatric / ultrasound

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