Diagnostic performance of four AI tools in pharmacology MCQs: Accuracy, sensitivity, and specificity
Bibliographic record
Abstract
BACKGROUND: The rapid rise of AI in medical and pharmaceutical education has engendered much interest; however, a knowledge gap still exists in the evaluation of performances of these tools in critical academic contexts. OBJECTIVES: The aim of this study was to assess and compare the performances of four openly accessible AI language tools, Microsoft Copilot, ChatGPT-3.5, Google Gemini, and DeepSeek AI, in responding to pharmacology-related MCQs with regard to diagnostic accuracy, sensitivity, specificity, and reproducibility. METHODS: A total of 80 MCQs were generated and validated, representing four therapeutic systems: cardiovascular, respiratory, gastrointestinal, and endocrine, including four pharmacological domains: mechanism of action, side effects, pharmacokinetics, and drug-drug interactions. Answers were classified into true/false positives and negatives in order to calculate accuracy, sensitivity, and specificity. After two weeks, a second round of testing was performed with the questions to assess answer reproducibility. RESULTS: The top overall performer was Microsoft Copilot: 87.5% accuracy, a sensitivity of 94.6%, and a specificity of 70.8%. It continued to perform strongly across all therapeutic systems, especially in the cardiovascular and respiratory domains, with the highest accuracy in identifying drug mechanisms and side effects. ChatGPT-3.5 performed similarly to Google Gemini (76.3% and 75.0% accuracy, respectively) but with higher sensitivity for ChatGPT-3.5 and higher specificity for Gemini. DeepSeek AI had the lowest accuracy overall (68.8%) and the lowest specificity (29.2%), but the highest consistency of reproducibility (97.5%). The performance of all tools decreased significantly with increasing level of question difficulty (p < 0.05). CONCLUSION: All tools have some value in pharmacology education, but Microsoft Copilot was the most consistently accurate. Limitations in complexity and reproducibility suggest that caution should be exercised in academic and clinical use, particularly given the variability seen with ChatGPT-3.5.
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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 | 0.039 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".