Artificial Intelligence in Medical Education: Perceptions of Romanian Medical Students in a Cross-Sectional Study
Abstract
Background The rapid integration of artificial intelligence (AI) into medical education has transformed learning environments and educational strategies. Understanding medical students’ perceptions regarding the educational utility, clinical applicability, and ethical implications of AI tools is essential for guiding their responsible integration into modern medical curricula. Methods A cross-sectional online survey was conducted among medical students across all six study years at Victor Babeș University of Medicine and Pharmacy Timișoara in May 2026 using a modified, validated instrument. Questionnaire domains demonstrated good-to-excellent internal consistency (Cronbach’s α=0.80–0.94). Data from 374 valid respondents were evaluated using non-parametric Kruskal–Wallis tests with Holm–Bonferroni-adjusted Dunn’s post-hoc contrasts and multivariable ordinary least squares linear regression. Results Overall, 95.98% (n=359) of students reported prior AI use, and 62.56% (n=234) indicated frequent or daily utilization. Students reported the highest agreement for AI saving time during information retrieval (83.42%) and assisting in understanding medical concepts (74.33%). Cross-sectional comparisons identified significant differences across study years for five items (ε2=0.031–0.052), including higher perceived time savings (padj=0.007) and effort reduction (padj=0.040) among 6th-year compared to 3rd-year students. In multivariable linear regression adjusting for demographic and academic covariates, a positive attitude toward AI was a strong independent predictor of perceived clinical utility (B=0.899, SE=0.042, β=0.755, p<0.001, R2=0.577). Conclusion Romanian medical students demonstrated high familiarity with AI applications and favorable perceptions of their educational and clinical utility, with positive attitudes strongly predicting perceived clinical usefulness. Cross-sectional variations emerged across specific items and study cohorts. Persistent concerns regarding accuracy, reliability, and academic overdependence highlight the need for structured AI literacy curricula and clear institutional guidelines.