The need for artificial intelligence curriculum in medical education: A Canadian cross-sectional study of future oncology trainees.
Bibliographic record
Abstract
e13583 Background: Emerging artificial intelligence (AI) technologies have diverse applications in medicine, with early evidence suggesting that AI tools can accurately perform key tasks in oncology. As AI tools advance towards clinical implementation, skills in how to use and interpret AI in a healthcare setting could become integral for physicians. This study seeks to assess exposure to AI in medical education among trainees interested in pursuing a career in oncology, and the need for AI education in medicine. Methods: A 32 question survey for Canadian undergraduate medical students was distributed to students at all 17 Canadian medical schools. The survey assessed the currently available and perceived need for learning opportunities about AI and barriers to educating about AI in medicine. Interviews were conducted with participants to provide narrative context to survey responses. Likert scale (LS) survey questions were scored from 1 (disagree) to 5 (agree), and analyzed using a two-sided one sample t-test vs a neutral value. Interview transcripts were analyzed using qualitative thematic analysis. Results are described as mean LS score ± standard deviation. Results: We received 486 responses from 17 of 17 medical schools. Of these respondents, 98 (20.2%) are willing to pursue a residency in an oncology-related field (pathology, radiology, general surgery, internal medicine, radiation oncology). Respondents agreed that AI applications in medicine would become common in the future (3.80±0.38) and would improve medicine (3.71±0.54). Further, respondents agreed that they would need to use and understand AI during their medical careers (3.76±0.572; 3.43±0.773), and that AI should be formally taught in medical education (3.43±0.756). In contrast, a significant number of participants indicated that they did not have any formal educational opportunities about AI (1.76±0.785) and that AI-related learning opportunities were inadequate (2.12±0.802). Interviews with 18 students were conducted. Emerging themes from the interviews were a lack of formal education opportunities and logistical challenges in adding AI to curriculum. Conclusions: A lack of educational opportunities about AI in medicine were identified across Canadian medical students. Given that medical students overwhelmingly believe that AI is important to the future of medicine, and AI tools are currently progressing towards clinical implementation, AI should be considered for inclusion in formal medical curriculum.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".