Comprehensive analysis of the performance of GPT-3.5 and GPT-4 on the American Urological Association self-assessment study program exams from 2012-2023.
Bibliographic record
Abstract
INTRODUCTION: Artificial intelligence (AI) applications, specifically generative pre-trained transformers, have shown potential in medical education and board-style examinations. To assess this capability, we conducted a study comparing the performance of GPT-3.5 and GPT-4 on the American Urological Association (AUA) 2022 self-assessment study program (SASP) exams from 2012-2023. METHODS: We used a standardized prompt to administer questions from the AUA SASP exams spanning 2012-2023, totalling 1679 questions. The performance of the two AI models, GPT-3.5 and GPT-4, was evaluated based on the number of questions answered correctly. Statistical analysis was performed using Fisher's exact test and independent sample t-tests to compare the performance of GPT-4 to that of GPT-3.5 among test years and urology topic areas. Percentile scores were not calculable, however, a score of 50% is required to acquire CME credits on AUA SASP exams. RESULTS: The analysis showed significantly superior performance by GPT-4, which scored above 50% across all exam years except 2018, with scores ranging from 48-64%. In contrast, GPT-3.5 consistently scored below this threshold, with scores ranging from 26-38%. The total combined score for GPT-4 was 55%, significantly higher than the 33% achieved by GPT-3.5 (odds ratio [OR] 2.5, 95% confidence interval [CI] 2.2-2.9, p<0.001). GPT-4 significantly outperformed GPT-3.5 among AUA SASP test years from 2012-2023 (mean difference 23, t(22) 14, 95% CI 19-26, p<0.001), as well as among urology topic areas (mean difference 21, t(52)=5.5, 95% CI 13-29, p<0.001). CONCLUSIONS: GPT-4 scored significantly higher than GPT-3.5 on the AUA SASP exams in overall performance, across all test years, and in various urology topic areas. This suggests improvement in evolving AI language models in answering clinical urology questions; however, certain aspects of medical knowledge and clinical reasoning remain challenging for AI language models.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".