Exploring the Role of LLMs Like ChatGPT in Pharmacy Education for Supporting Students' Therapeutic Decision-making

Abstract

OBJECTIVE: The aim of this study was to evaluate the utility of large-scale language models as training strategies for clinical student pharmacists, exploring their potential use in drug dosage adjustment while analyzing their limitations. METHODS: ChatGPT, Gemini, and Copilot were evaluated for predicting the appropriate drug dose using common pharmacokinetic problems. Three narrow therapeutic drugs-tacrolimus, vancomycin, and lidocaine-were selected, and 3 different therapeutic scenarios were tested for each drug with the models. The prompt structure was modified to analyze its impact on the results achieved. The performance of the models in each scenario was rated using a numerical scale from 0 to 2. The potential benefits of the model as support tools for students, as well as the identification of the current limitations, were evaluated. RESULTS: ChatGPT achieved the highest score and had the greatest number of correct answers. Tacrolimus inputs produced the most correct answers, likely because its calculations were less complex. Moreover, modifications in the prompt structure led to significant changes in the results for most models, highlighting the critical role of prompt design. CONCLUSION: While the results indicate room for improvement, the successful cases highlight promising directions. With more rigorous study of the models, enhanced data quality, and a deeper understanding of prompt design, these artificial intelligence tools could offer substantial support to students. Educating users on these emerging technologies will further enhance their application in health care, maximizing benefits and mitigating potential risks and limitations.

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