Perceptions and Use of Generative Artificial Intelligence in Medical Students: A Multicenter Survey
Bibliographic record
Abstract
Introduction: Generative artificial intelligence (AI) has transformative potential in medical training, and its role in medicine holds drastic implications for patients, healthcare providers, and society; however, its current use by medical students is unknown. The study aims to characterize the use, frequency of use, and perceptions of generative AI by Canadian medical students. Methods: A cross-sectional survey was distributed to 6 medical schools in Ontario, Canada, to investigate how medical students use generative AI in education, clinical settings, and for communication, and to assess the perceived barriers and enablers that influence their use. Results: A total of 167 respondents completed the survey (60.8% female, 69.3% in first and second year), and over 78.9% of respondents reported using generative AI, with ChatGPT being the most popular model; 53.0% of respondents were frequent users and reported using generative AI tools at least once a week. In clinical settings, students report using generative AI for learning and reviewing medical content, summarizing clinical guidelines, and generating differential diagnoses; 92.8% of students were willing to learn how to use generative AI to integrate it into their future clinical practice. At the same time, most medical students appreciated the limitations of generative AI in terms of its risk for inaccuracy (91.6%) and bias (78.9%); 75.9% of participants agreed that generative AI should be implemented as a resource or formal teaching topic in medical training. Discussion: The findings of this study may help guide medical education institutions in adapting curricula and developing policies to promote the ethical and appropriate use of generative AI in medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".