European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie · 2026
Artificial Intelligence Competencies and Educational Needs Among ERNICA Members: Results of a Multinational Survey.
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Till Holger, Elsayed Hesham, Obermüller Beate, Gnatzy Richard, Lacher Martin, Tschauner Sebastian, Verhoeven Rosa, Wijnen Rene M H, Singer Georg, ERNICA AI Task Force Members
Аннотация
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).
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.
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.