Open Access
CC BY 4.0 · Eur J Pediatr Surg
DOI: 10.1055/a-2787-2213
Original Article

Artificial Intelligence Competencies and Educational Needs Among ERNICA Members: Results of a Multinational Survey

Authors

  • Holger Till

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

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

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

    2   Department of Pediatric Surgery, University of Leipzig, Leipzig, Germany
  • Martin Lacher

    2   Department of Pediatric Surgery, University of Leipzig, Leipzig, Germany
  • Sebastian Tschauner

    3   Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
  • Rosa Verhoeven

    4   Division of Pediatric Surgery, Department of Surgery, University Medical Center Groningen, Groningen, The Netherlands
  • Rene M. H. Wijnen

    5   Department of Pediatric Surgery, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands
  • Georg Singer

    1   Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria
  • ERNICA AI Task Force Members

Abstract

Introduction

Artificial intelligence (AI) is increasingly recognized as a transformative force in healthcare. In the field of rare diseases, AI can enhance diagnostic accuracy and facilitate knowledge-sharing across borders. To effectively contribute to the development and use of AI-based medical support systems, clinicians must provide specialized AI competencies. This survey assesses the AI readiness, educational needs, and perceptions of members within the European Reference Network for Rare Inherited and Congenital Anomalies (ERNICA).

Materials and Methods

A structured online survey consisting of 22 questions was distributed to 389 ERNICA members, collecting data on demographics, AI awareness, current use, educational needs, concerns, and future expectations.

Results

A total of 89 members responded (23%), representing a multidisciplinary group with varying experience. Most respondents (94%) reported no formal AI training yet, and rated their AI knowledge as basic (66%) or intermediate (26%). About 48% of the participants stated using AI applications already. Key educational needs included online courses and webinars. Major concerns focused on the reliability and accuracy of AI tools (80%) and ethical implications (71%). At the same time, 55% expect ERNICA to take a leading role in AI education in the diagnosis and management of rare gastrointestinal diseases.

Conclusion

This survey among ERNICA members revealed a definite gap of AI understanding and training. Addressing these issues requires tailored educational initiatives focused on practical AI applications, ethical considerations, and interpretability. By adopting a proactive role in AI capacity-building, ERNICA could contribute to responsible and effective integration of AI into rare disease care.

These authors contributed equally to this article.


Note

The names of the ERNICA AI Task Force members are also provided in the supplementary material.




Publication History

Received: 08 September 2025

Accepted: 13 January 2026

Accepted Manuscript online:
20 January 2026

Article published online:
03 February 2026

© 2026. 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/)

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