A roadmap of artificial intelligence applications in pediatric surgery: a comprehensive review of applications, challenges, and ethical considerations

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming healthcare, with growing interest in their application to rare pediatric surgical conditions. In these settings, limited data availability often brakes traditional research. Although pediatric surgery has historically been slower than other specialties in adopting ML, recent years have seen an increase in AI-driven tools designed for surgical care. This review presents an overview of AI applications in pediatric surgery, highlighting current uses, benefits, challenges, and their potential clinical impact. A comprehensive literature search was conducted to identify studies on AI and ML models relevant to pediatric surgery. The findings indicate that ML is mainly applied in predictive decision support, particularly for preoperative risk stratification, intraoperative navigation, and postoperative outcome prediction. AI is especially valuable in endoscopic and minimally invasive procedures, where it enhances the visualization of anatomical landmarks. In pediatric oncologic surgery, AI aids in the accurate localization and delineation of tumors. Additionally, AI improves pathology workflows through automated image analysis and annotation, supporting both diagnosis and education. Despite these advances, ethical and regulatory challenges remain. Ensuring data privacy and obtaining informed consent are essential. When responsibly implemented, AI can significantly improve pediatric surgical care.