Awareness about the role of artificial intelligence in diagnostic radiology among medical students in Saudi Arabia: a crosssectional study
Bibliographic record
Abstract
Background: Artificial Intelligence (AI) is revolutionizing radiology practice by supporting diagnostic accuracy and efficiency, yet many radiologists have not integrated it into their daily workflows. However, literature on radiology AI knowledge and perceptions among medical students is limited, both globally and within Saudi Arabia. Objectives: This study evaluates the extent of knowledge and understanding of AI applications in radiology among medical students from various universities in Saudi Arabia. Methodology: We conducted a cross-sectional study involving 351 medical students from different universities in Saudi Arabia. The online questionnaire included demographic characteristics and questions regarding awareness of AI applications in radiology, as well as preferred learning methods. Descriptive statistics were used for basic understanding, and the chi-square test was employed to assess associations between demographic characteristics and awareness of AI. Results: The survey revealed that 66.9% of participants had some level of awareness regarding AI in radiology, whereas 33.1% had little or no knowledge of it. The presence of a dedicated radiology module in the curriculum was significantly associated with higher awareness of AI applications (p < 0.001). The preferred learning methods included lectures (47.3%), workshops (43.0%), and extracurricular activities (35.3%). A total of 59.3% of students agreed or strongly agreed that AI should be included in the medical curriculum; however, only a quarter felt they had adequate opportunities to learn about it. Conclusion: There is a high level of awareness of AI among medical students in Saudi Arabia, but this does not equate to comprehensive education on all aspects of AI use in radiology. The results highlight a lack of formalized AI training in medical schools, suggesting the need for its integration into the curriculum to prepare future physicians who will increasingly rely on this technology in their practice. A collaborative focus on both the technical and ethical aspects of AI is necessary as we continue to explore this issue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".