{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T11:38:59Z","timestamp":1789213139483,"version":"build-2803163510"},"reference-count":58,"publisher":"Georg Thieme Verlag KG","issue":"02","license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Eur J Pediatr Surg"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                  <jats:p>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.<\/jats:p>\n                  <jats:p>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.<\/jats:p>\n                  <jats:p>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.<\/jats:p>","DOI":"10.1055\/a-2689-8280","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T23:08:47Z","timestamp":1756249727000},"page":"157-164","source":"Crossref","is-referenced-by-count":2,"title":["Latest Developments in Artificial Intelligence and Machine Learning Models in General Pediatric Surgery"],"prefix":"10.1055","volume":"36","author":[{"given":"Hesham","family":"Elsayed","sequence":"additional","affiliation":[{"name":"Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georg","family":"Singer","sequence":"additional","affiliation":[{"name":"Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tristan","family":"Till","sequence":"additional","affiliation":[{"name":"Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Holger","family":"Till","sequence":"additional","affiliation":[{"name":"Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2025,9,5]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-025-11669-z","article-title":"Impact of test set composition on AI 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