European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie
January 20, 2026 · via Europe PMC
Artificial Intelligence Competencies and Educational Needs Among ERNICA Members: Results of a Multinational Survey.
Till H, Elsayed H, Obermüller B, +6 more, ERNICA AI Task Force Members.
AI summary — for doctors, generated from the abstract only
A multinational survey of 89 ERNICA network members found a substantial gap in formal AI training (94% reported none) despite nearly half already using AI applications, with major concerns about reliability/accuracy (80%) and ethics (71%) of AI tools in rare pediatric disease care.
What was studied
A structured 22-question online survey distributed to 389 members of the European Reference Network for Rare Inherited and Congenital Anomalies (ERNICA), assessing AI readiness, educational needs, current use, concerns, and future expectations regarding artificial intelligence in rare disease care.
Key findings
- 89 of 389 members responded (23% response rate).
- 94% reported no formal AI training, rating their AI knowledge as basic (66%) or intermediate (26%).
- About 48% reported already using AI applications in some capacity.
- Major concerns were reliability/accuracy of AI tools (80%) and ethical implications (71%); 55% expected ERNICA to take a leading role in AI education for rare gastrointestinal disease diagnosis and management.
Clinical relevance
The findings suggest there is a clear need for structured AI education programs for clinicians working in rare pediatric disease networks, given the gap between current AI use and formal training, which could inform how professional societies develop AI literacy initiatives.
Who should care
Limitations
The survey had a modest 23% response rate, which may not represent the full ERNICA membership's views, and the findings are based on self-reported perceptions rather than objective competency testing.
Most clinicians in this European rare-disease network use or encounter AI tools but lack formal training, highlighting an educational gap that professional networks may need to address.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
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).
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.
DOI
10.1055/a-2787-2213
PMID
41558661
PMCID
PMC13375283
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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