The study by Duci et al. explores how Artificial Intelligence (AI) technologies such as machine learning (ML), computer vision (CV), and natural language processing (NLP) are being applied in pediatric surgery to support diagnoses, guide intraoperative decision-making, and improve postoperative care. Researchers did a comprehensive literature review in PubMed for studies on artificial intelligence in pediatric surgery. They organized the results into three main categories: ML, CV, and NLP. The findings showed that AI is being used in predictive analysis, medical imaging, surgical support, and pathology. ML was generally categorized into 3 types (supervised, unsupervised, and reinforcement learning) and was often used to support clinical decisions and predict surgical outcomes. Various ML algorithms were able to achieve AUCs greater than 0.90 in diagnosing conditions like appendicitis, Hirschsprung disease, necrotizing enterocolitis (NEC), and urologic conditions although it performed less well with complex cases. It was also able to predict postoperative complications, such as Intensive Care Unit (ICU) admissions, with fair to strong predictive performances. CV algorithms have demonstrated improved imaging analysis helping with the diagnosis of conditions like Crohn’s disease and have facilitated complete resection of pediatric tumors with intraoperative, real-time tracking during surgeries. Finally, NLP algorithms have analyzed online patient narratives to improve communication and care strategies in adolescents. Overall, AI algorithms were shown to improve preoperative risk assessments and diagnosis, assist with decision-making during surgery, and predict postoperative outcomes. In these ways, AI shows significant promise as a complement to physician expertise in pediatric surgery.