Diagnostic Radiology Residency Application Trends: Canadian Match Results From 2010-2020
Bibliographic record
Abstract
Introduction: Rapid advancements in artificial intelligence (AI) have generated uncertainty about the future of radiology among medical students. However, it is unclear whether this has affected radiology residency applications. The purpose of this study was to evaluate recent trends in the Canadian radiology residency match. Methods: Canadian Resident Matching Service annual data reports from 2010-2020 were collected. Statistics were extracted for Canadian medical graduates applying to radiology in the R-1 main residency match and analyzed using linear regression. Results: The number of available radiology residency positions decreased ( P = .01); declining from 84 in 2010 to 81 in 2020 (mean = 83.1). The overall number of applicants did not change ( P = .08, mean = 131.8). The proportion of applicants with radiology as their first choice decreased ( P = .001); declining from 4.5% in 2010 to 3.1% in 2020 (mean = 3.4%). The number of applicants applying exclusively to radiology also decreased ( P = .02); declining from 39 in 2010 to 16 in 2020 (mean = 23). Positions per applicant ( P = 0.24, mean = 0.64), and positions per applicant with radiology as their first choice did not change ( P = 0.07, mean = 0.91). Conclusion: While the overall number of students applying to radiology did not change, the number of applicants ranking radiology as their first or only choice decreased sharply. This analysis corroborates recent reports of increased workload, burnout, and declining reimbursement as well as uncertainty about the future of radiology due to advances in AI.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".