ChatGPT performance on pharmacology examination and board review questions: Implications for medical education and knowledge assessment.
Objectives: This study aimed to evaluate ChatGPT's performance on pharmacology exam questions by assessing its accuracy in basic and clinical pharmacology, reasoning processes, and response consistency over time. Methods: A dataset of 583 multiple-choice questions from the Pharmacology Examination a...
| Publicado en: | Pharmacy Practice (1886-3655) Vol. 24; no. 2; pp. 1 - 11 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Centro de Investigaciones y Publicaciones Farmaceuticas S.L.
Apr-Jun2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195876372&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195876372 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1885642X 3EAU jtl: Pharmacy Practice (1886-3655) issn: 1885642X maglogo: N pubinfo: dt: Apr-Jun2026 vid: 24 iid: 2 pid: 36266 pub: Centro de Investigaciones y Publicaciones Farmaceuticas S.L. artinfo: ui: 195876372 195876372 195876372 10.18549/PharmPract.2026.2.3488 195876372 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: ChatGPT performance on pharmacology examination and board review questions: Implications for medical education and knowledge assessment. aug: au: Hijazeen, Rima A. Yousef, Al-Motassem Almousa, Ahmed Alzoghair, Aya N. Dwairi, Jude K. Sawaqed, Majd I. Ryahneh, Ghaith F. Al Ali, Marwan H. affil: PhD Clinical Pharmacy Practice Associate Professor in Clinical Pharmacy Practice, The University of Jordan Faculty of Pharmacy Department of Biopharmaceutics and Clinical Pharmacy, Amman 11942, Jordan sug: subj: Artificial Intelligence, Generative Evaluation Pharmacy and Pharmacology Education Computerized Educational Testing Education, Medical Knowledge Evaluation Human Descriptive Statistics Credentialing Examinations Educational Measurement Chi Square Test Clinical Reasoning Natural Language Processing Data Analysis Software McNemar's Test ab: Objectives: This study aimed to evaluate ChatGPT's performance on pharmacology exam questions by assessing its accuracy in basic and clinical pharmacology, reasoning processes, and response consistency over time. Methods: A dataset of 583 multiple-choice questions from the Pharmacology Examination and Board Review (13th edition) was used. ChatGPT's responses were evaluated for logical justification, use of internal question stem information, and integration of external knowledge. Statistical analyses, including chi-square and McNemar tests, assessed associations and changes in response accuracy over a four-week interval. Results: ChatGPT achieved 76.2% accuracy (444/583 questions), demonstrating logical reasoning in 97% of responses. Internal information was used in 99.7% of cases, while external information was incorporated in 98% of correct and 93.5% of incorrect responses (p = 0.008). Information errors were the most common reason for incorrect answers. A statistically significant improvement in accuracy upon re-evaluation (χ² = 37.3, p < 0.0001) was observed, suggesting potential temporal variation in performance. Conclusion: ChatGPT meets or exceeds typical passing standards in many educational settings, with evidence of improved response accuracy over time. These findings highlight its capabilities in processing pharmacological content, with potential implications for future research into AI-assisted educational tools. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|