Evidence map›Paper›PMID 39864950›Full record

ArticleJMIR medical education2025

Performance of ChatGPT-3.5 and ChatGPT-4 in the Taiwan National Pharmacist Licensing Examination: Comparative Evaluation Study.

Ying-Mei Wang, Hung-Wei Shen, Tzeng-Ji Chen, Shu-Chiung Chiang, Ting-Guan Lin

Abstract readComparative Study
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ying-Mei WangDepartment of Medical Education and Research, Taipei Veterans General Hospital Hsinchu Branch, 81, Section 1, Zhongfeng Road, Zhudong, Hsinchu, 310, Taiwan, 886 03-5962134 ext 127.ORCID 0009-0006-9823-5298
Hung-Wei ShenDepartment of Medical Education and Research, Taipei Veterans General Hospital Hsinchu Branch, 81, Section 1, Zhongfeng Road, Zhudong, Hsinchu, 310, Taiwan, 886 03-5962134 ext 127.ORCID 0009-0002-6095-1185
Tzeng-Ji ChenDepartment of Family Medicine, Taipei Veterans General Hospital Hsinchu Branch, Hsinchu, Taiwan.ORCID 0000-0002-8350-0232
Shu-Chiung ChiangDepartment of Medical Education and Research, Taipei Veterans General Hospital Hsinchu Branch, 81, Section 1, Zhongfeng Road, Zhudong, Hsinchu, 310, Taiwan, 886 03-5962134 ext 127.ORCID 0000-0002-8795-2228
Ting-Guan LinDepartment of Pharmacy, Taipei Veterans General Hospital Hsinchu Branch, Hsinchu, Taiwan.ORCID 0009-0006-9138-9075

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Educational MeasurementEducation, PharmacyLicensure, PharmacyPharmacistsGenerative Artificial IntelligenceHumansTaiwanAIartificial intelligenceChatbotchat generative pre-trained transformerChatGPTeducational measurementevaluation and comparison studygenerative AIGPT-4learning modelmachine learningmedical databasesmedical decision-makingmedical educationpharmacistpharmacypharmacy licensurestatistical analysesTaiwanTaiwan national pharmacist licensing examination

Identifiers

PMID39864950
PMCPMC11769692

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.