Latest Developments in Artificial Intelligence and Machine Learning Models in General Pediatric Surgery

Clinical Snapshot

Key Findings

• P Value: Not applicable for narrative review

• Effect Size: Not quantified - qualitative synthesis only

• Primary Outcome: AI/ML models show promising diagnostic and prognostic capabilities across pediatric surgical conditions

• Nnt Or Sensitivity: Individual study sensitivities and specificities mentioned but not synthesized

• Confidence Interval: Not provided

Clinical Application

Limited by small datasets, lack of external validation, and need for specialized training Relevant to Australian pediatric surgical centers, though implementation would require TGA approval for AI diagnostic tools and integration with existing clinical workflows Pediatric patients requiring general surgical evaluation, particularly for appendicitis, NEC, Hirschsprung's disease, CDH, and biliary atresia

Abstract

Artificial intelligence (AI) and machine learning (ML) models rapidly transform health care with applications ranging from diagnostic image interpretation, predictive modeling, personalized treatment planning, real-time intraoperative guidance, and outcome prediction. However, their implementation in general pediatric surgery remains limited due to the rarity and complexity of pediatric surgical conditions, small and heterogeneous datasets, and a lack of formal AI training and competencies among pediatric surgeons.This narrative review explores the current landscape of AI and ML applications in general pediatric surgery, focusing on five key conditions: appendicitis, necrotizing enterocolitis, Hirschsprung's disease, congenital diaphragmatic hernia, and biliary atresia. For each, we summarize recent developments, including the use of AI in image analysis, diagnostic support, prediction of disease severity and outcome, postoperative monitoring, and histopathological evaluation. We also highlight novel tools such as explainable AI models, natural language processing, and wearable technologies.Recent findings demonstrate promising diagnostic and prognostic capabilities across multiple conditions. However, most AI/ML models still require external validation and standardization. The review underscores the importance of collaborative, multicenter research based on joint datasets as well as targeted AI education for pediatric surgeons to fully explore the benefits of these technologies in clinical practice.AI and ML offer significant potential to improve pediatric surgical care, but broader implementation will require multicenter collaboration, a robust dataset, and targeted AI education for pediatric surgeons.

References

1. 1.Elsayed, H., Singer, G., Till, T., & Till, H. (2026). Latest developments in artificial intelligence and machine learning models in general pediatric surgery. European Journal of Pediatric Surgery, 40858266. https://doi.org/10.1055/a-2646-2052