Journal Article

Factors affecting medical artificial intelligence (AI) readiness among medical students: taking stock and looking forward

Motevaseli, Sayeh, Amani, Mohammad Amin, Yıldırım, Murat, Ziapour, Arash, Darabi, Fatemeh, Janjani, Parisa

2025

Overview

Background Measuring 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. Methods This 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. Results Most 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. Conclusion Our 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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Ability Identification / Adult / AI readiness / Artificial Intelligence / College Readiness / College Science / Cross-Sectional Studies / Curricula / Curriculum Development / Data Analysis / Data Collection / Education / Education, Medical, Undergraduate / Educational Needs / Educational Technology / Ethics / Evaluation / Extracurricular Activities / Female / Future of AI / Futuristic perspective / Humans / Influence of Technology / Iran / Learning Processes / Male / Marital Status / Medical Education / Medical Evaluation / Medical Schools / Medical students / Needs Assessment / Participant Characteristics / Physicians / Reliability / School Demography / School Readiness / Statistical Analysis / Student Characteristics / Student Participation / Students, Medical - psychology / Students, Medical - statistics & numerical data / Surveys and Questionnaires / Technology application / Theory of Medicine/Bioethics / Variance analysis / Young Adult