Malaysian Medical Students’ Attitudes and Readiness Toward AI (Artificial Intelligence): A Cross-Sectional Study
Open accessAbstract
OBJECTIVES: The Malaysian health ministry has started introducing artificial intelligence (AI) technology to aid local healthcare delivery. This study aims to survey Malaysian medical students’ attitudes toward AI and evaluate their readiness to work with medical AI technology. METHODS: An online questionnaire on Google Forms was distributed to all 31 medical schools in Malaysia. The questionnaire consists of 3 sections: the first part surveyed the participants’ demographics, the second assessed the participants’ attitudes toward AI, and the final part utilizes the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) scale to evaluate their AI readiness. RESULTS: Three hundred and one students from 17 universities in Malaysia responded to the questionnaire. 87.36% of students agreed that AI will play an essential role in healthcare; 32.55% of students were less likely to consider a career in radiology due to the advancement of AI. The majority of students (71%) felt that teaching in AI will benefit their careers, while 69.44% agreed that all students should receive teaching in AI. Around 44.5% of students felt that they will possess the knowledge required to work with AI upon graduation. On the MAIRS-MS scale, students had a mean score of 21 of 40 for the cognitive factor, 25 of 40 for the ability factor, 10 of 15 for the vision factor, and 11 of 15 for the ethics factor. Overall, Malaysian students had a mean total score of 67±14.8 out of 110. CONCLUSION: Malaysian medical students have demonstrated awareness of AI and a willingness to learn more about it. More work needs to be done to improve students’ AI readiness, particularly their knowledge and application of AI technology. Malaysian universities should start to work on incorporating AI teaching into their curricula.
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In Malaysia, only 31% of respondents reported feeling competent to apply AI practically.
“Only 31% of respondents in Malaysia and 18.4% in Jordan reported feeling competent to apply AI practically [44, 48].”
From Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis (2026) · cites this paper as [48]
The Results section reports that 31% of Malaysian medical students said they understood AI’s basic computational principles, not that they felt competent to apply AI practically. For the closer measure of practical use, the Results report that about 44% expected to be confident using basic healthcare AI tools at the end of their degrees. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe 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 [32]
MAIRS-MS has a Cronbach's alpha of 0.87.
“MAIRS-MS has a Cronbach’s alpha of 0.87”
In Jordan, only 18.4% of respondents reported feeling competent to apply AI practically.
“Only 31% of respondents in Malaysia and 18.4% in Jordan reported feeling competent to apply AI practically [44, 48].”
From Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis (2026) · cites this paper as [48]
The Results section reports findings from Malaysian medical students, including that 31% said they understood basic AI computational principles. A search for Jordan and 18.4% found no passage in our copy reporting the claimed Jordanian result, so this source cannot verify that part of the sentence. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Influential
- Authors
- Tung, A. Y. Z., & Dong, L. W.
- Affiliations
- Wrexham Maelor Hospital (GB)
- Corresponding author
- Tung, Alvin Yong Zong
- Venue
- Journal of Medical Education and Curricular Development
- Publisher
- SAGE Publications
- Published
- 2023-01
- Volume
- 10
- Pages
- 23821205231201164-23821205231201164
- ISSN
- 2382-1205 (ISSN-L)
- PMC
- PMC10501060
- OpenAlex
- W4386716960
- Cited by
- 84
- References
- 23
- Citations by year
- 2026: 25 · 2025: 41 · 2024: 16 · 2023: 2
- FWCI
- 3.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Medical Education and Curricular Development (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence in healthcare, medical students, attitudes toward AI, AI readiness, artificial intelligence in radiology
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