Readiness Towards Artificial Intelligence Among Undergraduate Medical Students in Malaysia
Open accessAbstract
Artificial intelligence (AI) technology is growing at a fast pace and permeates many aspects of people’s daily lives. Medical students’ inclination towards AI in the medical field increases the probability of successful AI adoption and its value in the medical field. This study was conducted to evaluate medical AI readiness among undergraduate medical students. A cross-sectional study was conducted from March 2022 to April 2022 in a private medical institution in Malaysia. A non-probability purposive sampling method was used to enroll students and a questionnaire was distributed online via Google Forms. The questionnaire, captioned “Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)”, was used for data collection. The analysis included frequency tables, percentages, standard deviation, unpaired t-test, and analysis of variance (ANOVA) test. Out of 105 participants, 67.62% scored 53 to 83, followed by 24.76%, who scored 84 to 114, and 7.62%, who scored 22 to 52 on the medical AI readiness scale. The mean of the total score of medical AI readiness obtained was 75.04. There were significant correlations between age and study year with the ability, vision, and ethics domains of medical AI readiness. A significant association was observed between previous training with all four domains of medical AI readiness. Policymakers and the educational sector should set up more AI training centers to provide and introduce basic courses on AI. More AI courses should be provided to younger populations to engage in AI digital information earlier, thus enabling them to acquire more confidence in interacting with AI technology in the future.
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The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was utilized in a few studies to explore perceptions of AI.
“The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) [30] was utilized in a few studies to explore perceptions of AI [31–34].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [33]
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- Authors
- Faculty of Medicine, M. U. C. M., Xuan, P. Y., Fahumida, M. I. F., Faculty of Medicine, M. U. C. M., Al Nazir Hussain, M. I., Faculty of Medicine, M. U. C. M., Jayathilake, N. T., Faculty of Medicine, M. U. C. M., Khobragade, S., Faculty of Medicine, M. U. C. M., Soe, H. H. K., Faculty of Medicine, M. U. C. M., Moe, S., Faculty of Medicine, M. U. C. M., Htay, M. N. N., & Faculty of Medicine, M. U. C. M.
- Affiliations
- Manipal University College Malaysia (MY)
- Corresponding author
- Fahumida, Mohamed Ismath Fathima
- Venue
- Education in Medicine Journal
- Publisher
- Universiti Sains Malaysia
- Published
- 2023-06-30
- Volume
- 15
- Issue
- 2
- Pages
- 49-60
- ISSN
- 2180-1932 (ISSN-L)
- OpenAlex
- W4383225406
- Cited by
- 41
- References
- 27
- Citations by year
- 2026: 14 · 2025: 17 · 2024: 9 · 2023: 1
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Education in Medicine Journal (publishedVersion)
- Funders
- Ege Üniversitesi
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- AI readiness, undergraduate medical students, AI training, AI ethics, artificial intelligence in healthcare, Malaysia
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