Artificial intelligence for prediction and detection of pediatric surgical site infection

Surgical site infections (SSIs) remain a significant source of morbidity in pediatric surgery, prolonging hospital stays, increasing readmissions, and driving up healthcare costs. Manual chart reviews and static risk models limit traditional SSI prediction and detection. The rise of artificial intelligence (AI), including machine learning (ML), natural language processing (NLP), and large language models (LLMs), offers a transformative opportunity to enhance prediction and surveillance. This review synthesizes current literature on AI applications in pediatric SSI, emphasizing predictive models built on NSQIP-P data and detection strategies leveraging EHRs and wearable technologies. Despite encouraging retrospective results, real-world adoption remains constrained by poor validation, limited generalizability, and workflow misalignment. Ethical and regulatory concerns, including bias, transparency, and pediatric-specific data limitations, must be addressed to ensure safe, equitable implementation. Thoughtfully developed and deployed, AI-driven tools can transform pediatric surgical care by enabling earlier intervention and improving outcomes.