Evidence map›Paper›PMID 40493330›Full record

ArticleInternational journal of clinical pharmacy2025

Accuracy and teachability of artificial intelligence chatbots in solving pharmaceutical calculations: a descriptive study.

Nicole Campbell, Julie Kalabalik-Hoganson

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Article in International journal of clinical pharmacy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Nicole CampbellDepartment of Pharmacy Practice, Fairleigh Dickinson University, Florham Park, NJ, USA. ncampbell@fdu.edu.ORCID http://orcid.org/0000-0003-4750-3118
Julie Kalabalik-HogansonDepartment of Pharmacy Practice, Fairleigh Dickinson University, Florham Park, NJ, USA.ORCID http://orcid.org/0000-0003-0158-3490

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPharmaceutical calculations are required elements of the Doctor of Pharmacy curriculum in the United States. With the growth of artificial intelligence chatbots, pharmacists and educators are exploring their application. The accuracy of artificial intelligence chatbots in performing pharmaceutical calculations remains unknown.

aimTo evaluate the accuracy of artificial intelligence chatbots for pharmaceutical calculations.

methodEleven free-access chatbots were tested using 7 faculty-generated questions: 1 control, 2 creatinine clearance, 1 oral to intravenous dose conversion, 2 antibiotic pharmacokinetic dosing, and 1 number needed to harm. Descriptive statistics were used to evaluate the primary outcome, which was proportion of correct responses. Secondary outcomes included types of errors and teachability.

resultsTen (90.9%) chatbots answered the control question correctly, and all answered the dose conversion question correctly. Eight (72.7%) chatbots correctly calculated number needed to harm. Only 1 (9.1%) provided the correct antibiotic dosing, and none correctly calculated creatinine clearance. Common errors included incorrect weight selection for creatinine clearance and use of incorrect formulas. Nine (81.8%) chatbots were teachable on at least 1 question.

conclusionArtificial intelligence chatbots demonstrated limited accuracy for multi-step pharmaceutical calculations and may be more reliable for low complexity calculations.

Indexed as

Artificial IntelligenceDrug Dosage CalculationsEducation, PharmacyAnti-Bacterial AgentsCurriculumGenerative Artificial IntelligenceHumansMedication ErrorsAnti-Bacterial AgentsArtificial intelligenceCalculationsChatbotsEducationPharmacokineticsPharmacy

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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 Socratic graph.