Knowledge of and Attitudes on Artificial Intelligence in Healthcare: A Provincial Survey Study of Medical Students
Bibliographic record
Abstract
ABSTRACT Background There has been growing acknowledgement that undergraduate medical education (UME) must play a formal role in instructing future physicians on the promises and limitations of artificial intelligence (AI), as these tools are integrated into medical practice. Methods We conducted an exploratory survey of medical students’ knowledge of AI, perceptions on the role of AI in medicine, and preferences surrounding the integration of AI competencies into medical education. The survey was completed by 321 medical students (13.4% response rate) at four medical schools in Ontario. Results Medical students are generally optimistic regarding AI’s capabilities to carry out a variety of healthcare functions, from clinical to administrative, with reservations about specific task types such as personal counselling and empathetic care. They believe AI will raise novel ethical and social challenges. Students are concerned about how AI will affect the medical job market, with 25% responding that it was actively impacting their choice of specialty. Students agree that medical education must do more to prepare them for the impact of AI in medicine (79%), and the majority (68%) believe that this training should begin at the UME level. Conclusions Medical students expect AI will be widely integrated into healthcare and are enthusiastic to obtain AI competencies in undergraduate medical education.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".