ArticleBMC medical education2026
Assessing the level of readiness for digital transformation in medicine: students' perspectives on the use of artificial intelligence in health : a cross-sectional study.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Translation, cultural adaptation, and validation of the perceived Artificial Intelligence Literacy Questionnaire-6 (PAILQ-6) in Arabic-speaking pharmacists: A cross-sectional methodological study.Exploratory research in clinical and social pharmacy · 2026Article
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4 authors.
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Abstract
backgroundArtificial intelligence (AI) is rapidly transforming healthcare by enhancing diagnostic accuracy, enabling personalized treatments, and improving patient outcomes. Medical students, as future healthcare providers and primary AI users, require adequate knowledge and readiness to integrate AI effectively in clinical practice. Despite growing global interest, little is known about the preparedness of medical students in Iran to adopt AI technologies.
methodsA descriptive cross-sectional survey was conducted among 321 students from medicine, dentistry, and pharmacy programs at Ahvaz Jundishapur University of Medical Sciences during the 2024–2025 academic year. Data were collected via a validated 22-item AI readiness scale covering four domains: cognition, competency, vision, and ethics. Descriptive and inferential statistics, including one-sample t-tests and Wilcoxon signed-rank tests, were applied based on data distribution. Correlation analyses explored relationships among readiness components.
resultParticipants demonstrated moderate cognitive readiness (mean = 3.03), indicating an average theoretical understanding of AI. Competency in AI application scored significantly above average (mean = 3.44, p < 0.001), reflecting confidence in practical use, particularly with digital health tools. The vision toward AI in medicine was positive (mean = 3.31, p < 0.001), although varied among students. Ethical awareness scored highest (mean = 3.69, p < 0.001), indicating strong sensitivity to AI’s ethical challenges. Significant positive correlations were found among all domains (r = 0.44 to 0.73, p < 0.01), with the strongest between cognition and competency. Despite general optimism, gaps remain in technical knowledge and regulatory understanding.
conclusionMedical students in this cohort demonstrate encouraging readiness to engage with AI, particularly in practical and ethical domains; however, foundational knowledge and technical literacy need to be strengthened. The findings underscore the urgent need to integrate interdisciplinary AI education, hands-on training, and legal-ethical instruction into medical curricula. These initiatives are essential to prepare future healthcare professionals for effective and responsible AI integration, ultimately enhancing the quality of patient care.
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