Evaluating accuracy and reproducibility of large language model performance on critical care assessments in pharmacy education

Evaluating accuracy and reproducibility of large language model performance on critical care assessments in pharmacy education

BackgroundLarge language models (LLMs) have demonstrated impressive performance on medical licensing and diagnosis-related exams. However, comparative evaluations to optimize LLM performance and ability in the domain of comprehensive medication management (CMM) are lacking. The purpose of this evalu...

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Bibliographic Details
Main Authors: Huibo Yang, Mengxuan Hu, Amoreena Most, W. Anthony Hawkins, Brian Murray, Susan E. Smith, Sheng Li, Andrea Sikora
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Artificial Intelligence
Subjects:
large language model
artificial intelligence
pharmacy
education
critical care
medical education
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2024.1514896/full
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https://www.frontiersin.org/articles/10.3389/frai.2024.1514896/full

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