ArticleJMIR medical education2025
Performance of ChatGPT-3.5 and ChatGPT-4 in the Taiwan National Pharmacist Licensing Examination: Comparative Evaluation Study.
Article in JMIR medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI-Based Assessment Tools in Pharmacy Education.Research square · 2026Article
- MolProphecy: Bridging medicinal chemists' knowledge and molecular pre-trained models via a multi-modal framework.Journal of advanced research · 2026Article
- Performance Evaluation of 18 Generative AI Models (ChatGPT, Gemini, Claude, and Perplexity) in 2024 Japanese Pharmacist Licensing Examination: Comparative Study.JMIR medical education · 2025Article
- Benchmarking ChatGPT-3.5 and OpenAI o3 Against Clinical Pharmacists: Preliminary Insights into Clinical Accuracy, Sensitivity, and Specificity in Pharmacy MCQs.Healthcare (Basel, Switzerland) · 2025Article
- The rise of AI in healthcare education: DeepSeek and GPT-4o take on the 2024 Taiwan Pharmacist Exam.Journal of the Chinese Medical Association : JCMA · 2025Article
- Diagnostic performance of four AI tools in pharmacology MCQs: Accuracy, sensitivity, and specificity.PloS one · 2025Article
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5 authors.
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Abstract
Background: OpenAI released versions ChatGPT-3.5 and GPT-4 between 2022 and 2023. GPT-3.5 has demonstrated proficiency in various examinations, particularly the United States Medical Licensing Examination. However, GPT-4 has more advanced capabilities. Objective: This study aims to examine the efficacy of GPT-3.5 and GPT-4 within the Taiwan National Pharmacist Licensing Examination and to ascertain their utility and potential application in clinical pharmacy and education. Methods: The pharmacist examination in Taiwan consists of 2 stages: basic subjects and clinical subjects. In this study, exam questions were manually fed into the GPT-3.5 and GPT-4 models, and their responses were recorded; graphic-based questions were excluded. This study encompassed three steps: (1) determining the answering accuracy of GPT-3.5 and GPT-4, (2) categorizing question types and observing differences in model performance across these categories, and (3) comparing model performance on calculation and situational questions. Microsoft Excel and R software were used for statistical analyses. Results: GPT-4 achieved an accuracy rate of 72.9%, overshadowing GPT-3.5, which achieved 59.1% (P<.001). In the basic subjects category, GPT-4 significantly outperformed GPT-3.5 (73.4% vs 53.2%; P<.001). However, in clinical subjects, only minor differences in accuracy were observed. Specifically, GPT-4 outperformed GPT-3.5 in the calculation and situational questions. Conclusions: This study demonstrates that GPT-4 outperforms GPT-3.5 in the Taiwan National Pharmacist Licensing Examination, particularly in basic subjects. While GPT-4 shows potential for use in clinical practice and pharmacy education, its limitations warrant caution. Future research should focus on refining prompts, improving model stability, integrating medical databases, and designing questions that better assess student competence and minimize guessing.
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