Perceived Challenges of Artificial Intelligence in \nHealthcare among Undergraduate Medical Students at a \nPublic Medical School in Sarawak, Malaysia
Bibliographic record
Abstract
Introduction: As artificial intelligence (AI) becomes more integrated into healthcare, it also brings challenges. Understanding these perceived challenges among medical students is crucial for developing educational frameworks that prepare them to navigate these \nchallenges in clinical practice. However, the perceived challenges of AI in healthcare among medical students in Sarawak, Malaysia, remain underexplored. \nAim/Purpose/Objective: This study aimed to assess the perceived challenges of AI in healthcare among undergraduate medical students at a public medical school in Sarawak, \nMalaysia. \n \nMethod: A mixed-method cross-sectional survey was conducted from October 2023 to August 2024 among 185 undergraduate medical students from year one to year five at a public medical school in Sarawak. A convenience sampling method was employed. Data were collected using a validated questionnaire adapted from a previous Canadian study \nassessing medical students’ perceived challenges of AI in healthcare. Participants rated their agreement on a 5-point Likert scale. Quantitative responses were analysed descriptively while qualitative data from open-ended questions were thematically analysed \n \nResults: Most students expressed concerns about AI-related challenges: 73.0% (30.3% strongly agree, 42.7% agree) supported the statement that “AI in medicine will raise new \nethical challenges” while 79.5% (30.3% strongly agree, 49.2% agree) supported that “AI in medicine will raise new social challenges.” Additionally, 73.5% (27.0% strongly agree, \n46.5% agree) supported that “AI in medicine will raise new challenges around health equity.” In contrast, only 22.2% (6.5% strongly agree, 15.7% agree) supported that “The \nMalaysian healthcare system is currently well prepared to deal with challenges having to do with AI”. Qualitative thematic analysis highlighted key themes of “Ethical, privacy, and security issues” and “Trust and reliability concerns”. \n \nConclusion: Most of the medical students in this study expressed concerns about challenges of AI in healthcare, especially in ethical, privacy and security challenges. Comprehensive AI training, including ethical guidelines, is needed to equip future healthcare professionals to address these challenges effectively. \n \nKeywords: Artificial intelligence; challenges; healthcare; medical students; medical \neducation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".