Medizinische Universität Graz - Research portal

Go directly to the nagivation.
Go directly to the content.

Navigation/Search

Selected Publication:

SHR Neuro Cancer Cardio Lipid Metab Microb

Elsayed, H; Singer, G; Till, T; Till, H.
Latest developments of Artificial Intelligence (AI) and Machine learning (ML) models in general pediatric surgery.
Eur J Pediatr Surg. 2025; Doi: 10.1055/a-2689-8280
PubMed FullText FullText_MUG

Leading authors Med Uni Graz: Elsayed Hesham; Till Holger
Co-authors Med Uni Graz: Singer Georg; Till Tristan
Altmetrics:
Dimensions Citations:
Plum Analytics:
Scite (citation analytics):
Abstract:: INTRODUCTION: Artificial intelligence (AI) and machine learning (ML) models rapidly transform healthcare 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. MATERIAL AND METHODS: This narrative review explores the current landscape of AI and ML applications in general pediatric surgery, focusing on five key conditions: appendicitis, necrotizing enterocolitis (NEC), Hirschsprung's disease, congenital diaphragmatic hernia (CDH) and biliary atresia (BA). 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. RESULTS: 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. CONCLUSION: AI and ML offer significant potential to improve pediatric surgical care, but broader implementation will require multicenter collaboration, robust dataset and targeted AI education for pediatric surgeons.