2026/06/05 by Nada Abedin, Peter Hahn, Stefan Zeuzem +1 · 1 voice
Medicine · Social Sciences · Health Professions · #Artificial Intelligence in Healthcare and Education #Ethics and Social Impacts of AI #Electronic Health Records Systems
paper · pdf · doi:10.1371/journal.pdig.0001433
openalex publication_date 2026/06/05 · openalex created_date 2026/06/06 · openalex updated_date 2026/07/27
Artificial Intelligence (AI) is rapidly transforming medical practice, with successful integration critically depending on healthcare professionals' self-reported perceived knowledge, attitudes, and perceived barriers to adoption. Despite rapid technological advances, comprehensive assessments of healthcare professionals' AI readiness remain limited, particularly regarding the relationship between enthusiasm and self-reported competency. We conducted a cross-sectional online survey of 148 healthcare professionals. The survey assessed demographics, AI knowledge/attitudes (12 Likert-scale items), institutional readiness, learning preferences, and perceived barriers. Inferential statistics including correlation analyses, Kruskal-Wallis H tests, and Mann-Whitney U tests were performed to examine relationships between attitudes and contextual factors. Participants (mean age 36.7 ± 8.1 years, 61.5% male, 77.7% from Germany) demonstrated a significant knowledge-enthusiasm gap. While 86.5% believed AI will transform medical practice and 76.4% expressed excitement about AI changes, only 20.3% felt well-informed about healthcare AI and 38.6% had medical AI experience. Correlation analysis revealed strong positive associations among enthusiasm measures (r = 0.63-0.88, p < 0.001) but weak correlations between knowledge and enthusiasm (r < 0.20), providing evidence consistent with the knowledge-enthusiasm gap. Institutional AI stance significantly affected individual knowledge levels (Kruskal-Wallis H(3) = 28.11, p < 0.001), but not enthusiasm. Primary barriers included knowledge deficits among leadership (62.8% institutional level), infrastructure limitations (52.0%), and system integration challenges (57.4%). Healthcare professionals, particularly in a German healthcare context, demonstrate strong enthusiasm for AI integration but face significant knowledge gaps and institutional barriers. While these findings might be most directly applicable to similar healthcare contexts, the identified knowledge-enthusiasm gap represents a critical target for educational interventions in similar well-resourced European healthcare systems. Successful AI implementation requires multi-level strategies addressing leadership education, infrastructure development, and hands-on training programs. These findings provide evidence-based guidance for healthcare institutions, educators, and policymakers developing AI adoption strategies.