Evidence map›Paper›PMID 37719325›Full record

ArticleJournal of medical education and curricular development

Malaysian Medical Students' Attitudes and Readiness Toward AI (Artificial Intelligence): A Cross-Sectional Study.

Alvin Yong Zong Tung, Ley Wen Dong

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Article in Journal of medical education and curricular development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 3 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

49 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Alvin Yong Zong TungFaculty of Medical Sciences, Newcastle University Medicine Malaysia, Johor Bahru, Malaysia.ORCID https://orcid.org/0000-0001-6783-0445
Ley Wen DongWrexham Maelor Hospital, Wrexham, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe 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.

methodsAn 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.

resultsThree 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.

conclusionMalaysian 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.

Indexed as

artificial intelligenceeducationmedical students

Identifiers

PMID37719325
PMCPMC10501060

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.