PDF herunterladen
Open Access
CC BY 4.0 · Eur J Pediatr Surg
DOI: 10.1055/a-2702-1843
Review Article

Explainable AI: Ethical Frameworks, Bias, and the Necessity for Benchmarks

Authors

  • Rosa Verhoeven

    1   Department of Surgery, Division of Pediatric Surgery, University Medical Center Groningen, University of Groningen, The Netherlands

  • Wiam Bouisaghouane

    2   Division of Neonatology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

  • Jan BF Hulscher

    1   Department of Surgery, Division of Pediatric Surgery, University Medical Center Groningen, University of Groningen, The Netherlands

Funding Information This work has been supported by the For Wis(h)dom Foundation (Project 9; February 2, 2022).
Weitere Informationen
Auch verfügbar auf
  Lizenzen und Reprints


Graphical Abstract

Preview

Abstract

Artificial intelligence (AI) is increasingly integrated into pediatric healthcare, offering opportunities to improve diagnostic accuracy and clinical decision-making. However, the complexity and opacity of many AI models raise concerns about trust, transparency, and safety, especially in vulnerable pediatric populations. Explainable AI (XAI) aims to make AI-driven decisions more interpretable and accountable. This review outlines the role of XAI in pediatric surgery, emphasizing challenges related to bias, the importance of ethical frameworks, and the need for standardized benchmarks. Addressing these aspects is essential to developing fair, safe, and effective AI applications for children. Finally, we provide recommendations for future research and implementation to guide the development of robust and ethically sound XAI solutions.

Keywords

explainable AI - machine learning - bias - pediatric surgery - benchmarks


Publikationsverlauf

Eingereicht: 23. Juli 2025

Angenommen: 13. September 2025

Artikel online veröffentlicht:
23. 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