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

Journal: European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie
Published Date: Jan 20, 2026

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 competen-cies. This survey assesses the AI readiness, educational needs and perceptions of members within the European Reference Network for Rare Inherited and Congenital Anomalies (ER-NICA). MATERIAL AND METHODS: A structured online survey consisting of 22 questions was dis-tributed 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%). 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 ethi-cal 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 amongst ERNICA members revealed a definite gap of AI un-derstanding and training. Addressing these issues requires tailored educational initiatives fo-cused 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.

Authors

  • Holger Till
    Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria.
  • Hesham Elsayed
    Department of Pediatric and Adolescent Surgery, Medical University Graz, Graz, Austria.
  • Beate Obermüller
    Department of Pediatric and Adolescent Surgery, Medical University Graz, Graz, Austria.
  • Richard Gnatzy
    Department of Pediatric Surgery, Leipzig University, Leipzig, Germany.
  • Martin Lacher
    Department of Pediatric Surgery, Leipzig University, Leipzig, Germany.
  • Sebastian Tschauner
    Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria.
  • Rosa Verhoeven
    Department of Surgery, Division of Pediatric Surgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
  • Rene Wijnen
    Department of Pediatric Surgery, Erasmus MC Sophia, Rotterdam, Netherlands.
  • Georg Singer
    Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria.

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