PDF herunterladen
Open Access
CC BY 4.0 · Eur J Pediatr Surg
DOI: 10.1055/a-2689-8280
Review Article

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

Autor*innen

  • Hesham Elsayed

    1   Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria

  • Georg Singer

    1   Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria

  • Tristan Till

    2   Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria

  • Holger Till

    1   Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria
Weitere Informationen
Auch verfügbar auf
  Lizenzen und Reprints

Abstract

Introduction

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.

Materials 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, 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.

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, a robust dataset, and targeted AI education for pediatric surgeons.

Keywords

machine learning - artificial intelligence - pediatric surgery - review


Publikationsverlauf

Eingereicht: 21. Juli 2025

Angenommen: 24. August 2025

Accepted Manuscript online:
26. August 2025

Artikel online veröffentlicht:
05. September 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany