Evaluation of large language models' ability to identify clinically relevant drug-drug interactions and generate high-quality clinical pharmacotherapy recommendations.

Journal: American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
Published Date: Jul 1, 2025

Abstract

PURPOSE: Large language models (LLMs) are promising artificial intelligence (AI) tools to support clinical decision-making. The ability of LLMs to evaluate medication regimens, identify drug-drug interactions (DDIs), and provide clinical recommendations has undergone limited evaluation. The purpose of this study was to compare the performance of 3 LLMs in recognizing DDIs, determining clinical relevance, and generating management recommendations.

Authors

  • Aaron Chase
    Department of Pharmacy, Augusta University Medical Center/UGA College of Pharmacy, Augusta, GA, USA.
  • Amoreena Most
    University of Georgia College of Pharmacy, Augusta, GA, United States.
  • Andrea Sikora
    Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA, United States.
  • Susan E Smith
    Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Athens, GA, United States.
  • John W Devlin
    Northeastern University School of Pharmacy, Boston, MA, USA.
  • Shaochen Xu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Tianming Liu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Brian Murray
    University of Colorado Skaggs Schools of Pharmacy and Pharamceutical Sciences, Aurora, CO, United States.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals