Evaluation of large language models’ ability to identify clinically relevant drug-drug interactions and generate high-quality clinical pharmacotherapy recommendations
While LLMs demonstrate promising potential to identify DDIs, application to clinical cases requires ongoing development. Findings from this study may assist in future development and refinement of LLMs for clinical decision-making related to DDIs.
Authors
- Susan Smith (ORCID: https://orcid.org/0000-0002-5171-8405)
- Brian Murray (ORCID: https://orcid.org/0000-0001-6660-9749)
- John W. Devlin (ORCID: https://orcid.org/0000-0003-1751-9012)
- Aaron Chase (ORCID: https://orcid.org/0000-0002-5891-4492)
- Amoreena Most (ORCID: https://orcid.org/0000-0002-1958-5374)
- Tianming Liu (ORCID: https://orcid.org/0000-0003-2245-7231)
- Shaochen Xu (ORCID: https://orcid.org/0009-0008-0802-9218)
- Andrea Sikora
Institutions
- Brigham and Women's Hospital (US)
- Northeastern University (US)
- Georgia College & State University (US)
- University of Georgia (US)
- Augusta University (US)
- Augusta University Health (US)
- University of Montana (US)
Publication Details
- Journal
- American Journal of Health-System Pharmacy
- Published
- 2025-06-27
- DOI
- https://doi.org/10.1093/ajhp/zxaf168
- Citations
- 5
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 4.03
Funders
- Agency for Healthcare Research and Quality