ArticleBMC medical education2025
Factors affecting medical artificial intelligence (AI) readiness among medical students: taking stock and looking forward.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Medical Students' Attitudes, Perceptions, and Self-Reported Familiarity With AI in Health Care: Systematic Review and Meta-Analysis.JMIR medical education · 2026Pooled it
- Medical undergraduate students' readiness and anxiety toward artificial intelligence: a systematic review and meta-analysis.BMC medical education · 2025Pooled it
- AI literacy and ethical attitudes toward AI in medical students: evidence from a structural model.BMC medical education · 2026Article
- Explore factors affecting students' acceptance of AI resurrection and ethical dilemmas in AI resurrection technology among Chinese college students: a cross-sectional study.BMC psychology · 2026Article
- Utilization of AI Among Medical Students and Development of AI Education Platforms in Medical Institutions: Cross-Sectional Study.JMIR human factors · 2026Article
- Artificial intelligence readiness and its association with artificial intelligence literacy among Chinese medical students: a latent profile analysis.Frontiers in public health · 2026Article
- Perceived knowledge, attitude, and practice of artificial intelligence among medical students in Guangxi: a cross-sectional study.Frontiers in public health · 2026Article
- Inclusive Leadership, Psychological Safety, AI Readiness, and Nurses' AI Literacy: A Longitudinal Study.Journal of nursing management · 2026Article
- Translation and psychometric validation of the Medical Artificial Intelligence Readiness Scale (MAIRS-MS) for Chinese medical students.BMC nursing · 2025Article
- Knowledge, Readiness, and Perception of Medical Students Toward Medical Artificial Intelligence: A Cross-Sectional Study.Journal of medical education and curricular developmentArticle
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6 authors.
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Abstract
backgroundMeasuring artificial intelligence (AI) readiness among medical students is essential to assess how prepared future doctors are to work with AI technology. Therefore, this study aimed to examine the factors influencing AI readiness among medical students at Kermanshah University of Medical Sciences, both by evaluating the current situation and considering future developments.
methodsThis was a cross-sectional descriptive-analytical study. The statistical population consisted of 800 first- to fifth-year medical students selected through convenient sampling at Kermanshah University of Medical Sciences from November to March 2023. The data collection tools were demographic checklists and Persian version questionnaire of the medical artificial intelligence readiness scale for medical students (MAIRS-MS). The data were analyzed at a significance level of P < 0.05 using independent t-test, and analysis of variance (ANOVA) tests through SPSS-24 software.
resultsMost of the students were male (56.13%). The overall score for medical AI readiness was 70.59 ± 19.24 out of a maximum possible score of 110. Students had the highest mean score of 9.73 ± 2.96 out of 15 in vision and the lowest mean score of 25.74 ± 7.52 out of 40 in ability. The overall mean of AI readiness (71.84 ± 18.27) was higher in females than males (69.62 ± 19.93), but this difference was not significant (p = 0.106). Furthermore, the mean total score of AI readiness increased with the increasing age of the students.
conclusionOur findings underscore the need to prepare students to work with AI technologies and to provide them with the essential knowledge and skills across different areas of AI. Accordingly, the Kermanshah University of Medical Sciences student's education unit should set up more AI training centers to provide and introduce basic artificial intelligence courses. Moreover, universities should identify the needs of students based on scientific evidence, and the medical education system should design AI training programs in its educational framework in the same direction.
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