Performance of artificial intelligence on a simulated Canadian urology board exam
Bibliographic record
Abstract
INTRODUCTION: Generative artificial intelligence (AI) has proven to be a powerful tool with increasing applications in clinical care and medical education. ChatGPT has performed adequately on many specialty certification and knowledge assessment exams. The objective of this study was to assess the performance of ChatGPT 4 on a multiple-choice exam meant to simulate the Canadian urology board exam. METHODS: Graduating urology residents representing all Canadian training programs gather yearly for a mock exam that simulates their upcoming board-certifying exam. The exam consists of written multiple-choice questions (MCQs) and an oral objective structured clinical examination (OSCE). The 2022 exam was taken by 29 graduating residents and was administered to ChatGPT 4. RESULTS: ChatGPT 4 scored 46% on the MCQ exam, whereas the mean and median scores of graduating urology residents were 62.6%, and 62.7%, respectively. This would place ChatGPT's score 1.8 standard deviations from the median. The percentile rank of ChatGPT would be in the sixth percentile. ChatGPT scores on different topics of the exam were as follows: oncology 35%, andrology/benign prostatic hyperplasia 62%, physiology/anatomy 67%, incontinence/female urology 23%, infections 71%, urolithiasis 57%, and trauma/reconstruction 17%, with ChatGPT 4's oncology performance being significantly below that of postgraduate year 5 residents. CONCLUSIONS: ChatGPT 4 underperforms on an MCQ exam meant to simulate the Canadian board exam. Ongoing assessments of the capability of generative AI is needed as these models evolve and are trained on additional urology content.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".