Comparative assessment of AI models in addressing questions on priapism
Bibliographic record
Abstract
INTRODUCTION: This study aimed to evaluate the performance of three artificial intelligence (AI) models - ChatGPT, Gemini, and Copilot - in addressing priapism-related inquiries. The accuracy, comprehensiveness, and clinical applicability of AI-generated responses were systematically analyzed. METHODS: Frequently asked questions (FAQs) regarding priapism were collected from medical guidelines, literature, and online health platforms. Each AI model generated responses, which were independently assessed by two experts based on accuracy, fluency, and clinical relevance. The Global Quality Score (GQS) was used for evaluation. Statistical analysis was performed using one-way ANOVA, with a significance threshold of p<0.05. RESULTS: ChatGPT and Gemini demonstrated comparable performance across all thematic categories, with mean scores ranging from 4.5-4.9, while Copilot showed significantly lower scores (3.2-4.2, p<0.001). Both ChatGPT and Gemini provided clinically relevant and accurate information, whereas Copilot's responses frequently lacked guideline-based recommendations. CONCLUSIONS: ChatGPT and Gemini were statistically comparable in generating reliable, clinically useful responses, making them valuable tools for medical education and patient counseling. Copilot, however, exhibited lower accuracy and applicability. These findings highlight the need for continuous refinement of AI models to enhance their role in clinical decision-making while ensuring human expertise remains central to patient care.
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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 | 0.038 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".