Knowledge, Attitudes, and Perceptions of Artificial Intelligence in Healthcare Among Medical Students at Sultan Qaboos University
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Bibliographic record
Abstract
Background Artificial intelligence (AI) is increasingly used in healthcare, but more data are needed about the knowledge, perceptions, attitudes, and preparedness of medical students in Oman towards this technology. This study aimed to investigate these aspects among clinical-year medical students at Sultan Qaboos University. Methodology A web-based validated exploratory questionnaire adapted from a study conducted at the University of Toronto was distributed to all clinical year (phase III) medical students at Sultan Qaboos University. The questionnaire collected demographic and background information, tested students' knowledge of AI, and assessed their perceptions and attitudes toward it. The data were analyzed using the Statistical Package for Social Sciences (SPSS, IBM Corp., Armonk, NY). Results A total of 221 out of 368 clinical-year medical students (60%) completed the survey. Most respondents were in their junior clerkship year (n = 94, 42.5%). Most students (n = 167, 75.4%) had no prior exposure to AI in healthcare, with a median knowledge score of 3.25 out of 5 in AI, and showed no improvement over the years. However, they overall had positive perceptions and attitudes towards AI. Students also had concerns about the impact of AI on employment prospects and ethical issues but were generally receptive to incorporating AI into medical school curricula, as 174 students (78.7%) believed every medical trainee should receive training on AI competencies. Conclusion This study provides valuable insights into the knowledge, perceptions, attitudes, and preparedness of medical students in Oman toward AI in healthcare. Medical educators in Oman should consider incorporating AI into medical school curricula to prepare future physicians for using this technology in healthcare.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it