Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Performance of GPT-3.5 and GPT-4 on the Korean Pharmacist Licensing Examination: Comparison Study
11
Zitationen
2
Autoren
2024
Jahr
Abstract
Background: ChatGPT, a recently developed artificial intelligence chatbot and a notable large language model, has demonstrated improved performance on medical field examinations. However, there is currently little research on its efficacy in languages other than English or in pharmacy-related examinations. Objective: This study aimed to evaluate the performance of GPT models on the Korean Pharmacist Licensing Examination (KPLE). Methods: We evaluated the percentage of correct answers provided by 2 different versions of ChatGPT (GPT-3.5 and GPT-4) for all multiple-choice single-answer KPLE questions, excluding image-based questions. In total, 320, 317, and 323 questions from the 2021, 2022, and 2023 KPLEs, respectively, were included in the final analysis, which consisted of 4 units: Biopharmacy, Industrial Pharmacy, Clinical and Practical Pharmacy, and Medical Health Legislation. Results: The 3-year average percentage of correct answers was 86.5% (830/960) for GPT-4 and 60.7% (583/960) for GPT-3.5. GPT model accuracy was highest in Biopharmacy (GPT-3.5 77/96, 80.2% in 2022; GPT-4 87/90, 96.7% in 2021) and lowest in Medical Health Legislation (GPT-3.5 8/20, 40% in 2022; GPT-4 12/20, 60% in 2022). Additionally, when comparing the performance of artificial intelligence with that of human participants, pharmacy students outperformed GPT-3.5 but not GPT-4. Conclusions: In the last 3 years, GPT models have performed very close to or exceeded the passing threshold for the KPLE. This study demonstrates the potential of large language models in the pharmacy domain; however, extensive research is needed to evaluate their reliability and ensure their secure application in pharmacy contexts due to several inherent challenges. Addressing these limitations could make GPT models more effective auxiliary tools for pharmacy education.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.740 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.649 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.202 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.886 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.