Large Language Models and the North American Pharmacist Licensure Examination (NAPLEX) Practice Questions

Abstract

Objective

This study aims to test the accuracy of large language models (LLMs) in answering standardized pharmacy examination practice questions.

Methods

The performance of 3 LLMs (generative pretrained transformer [GPT]−3.5, GPT-4, and Chatsonic) was evaluated on 2 independent North American Pharmacist Licensure Examination practice question sets sourced from McGraw Hill and RxPrep. These question sets were further classified into binary question categories of adverse drug reaction (ADR) questions, scenario questions, treatment questions, and select-all questions. Python was used to run χ² tests to compare model and question-type accuracy.

Results

Of the 3 LLMs tested, GPT-4 achieved the highest accuracy, with 87% accuracy on the McGraw Hill question set and 83.5% accuracy on the RxPrep question set. In comparison, GPT-3.5 had 68.0% and 60.0% accuracy on those question sets, respectively, and Chatsonic had 60.5% and 62.5% accuracy on those question sets, respectively. All models performed worse on select-all questions compared with non-select-all questions (GPT-3: 42.3% vs 66.2%; GPT-4: 73.1 vs 87.2%; Chatsonic: 36.5% vs 71.6%). GPT-4 had statistically higher accuracy in answering ADR questions (96.1%) compared with non-ADR questions (83.9%).

Conclusion

Our study found that GPT-4 outperformed GPT-3.5 and Chatsonic in answering North American Pharmacist Licensure Examination pharmacy licensure examination practice questions, particularly excelling in answering questions related to ADRs. These results suggest that advanced LLMs such as GPT-4 could be used for applications in pharmacy education.