Health AI Central
Peer-Reviewed Publication
medRxiv2025December 29, 2025Journal Article

Drug-drug interaction identification using large language models.

Kaitlin Blotske1, Xingmeng Zhao1, Kelli Henry1, Yanjun Gao1, Adeleine Tilley2, Moriah Cargile2, Brian Murray2, Susan E Smith3, Erin F Barreto4, Seth Bauer5, Sunghwan Sohn4, Tianming Liu6, Tell Bennett7, Mitch Cohen8, Andrea Sikora1,9
1University of Colorado School of Medicine, Department of Biomedical Informatics.
2University of Colorado Anschutz Medical Campus, Skaggs School of Pharmacy and Pharmaceutical Sciences. Department of Clinical Pharmacy, Aurora, CO, USA.
3University of Georgia College of Pharmacy, Department of Clinical and Administrative Pharmacy, Athens, GA, USA.
4Mayo Clinic, Rochester, MN, USA.
5Cleveland Clinic, Cleveland, OH, USA.
6Department of Computer Science, University of Georgia, Athens, GA.
7University of Colorado School of Medicine, Department of Biomedical Informatics Aurora, CO, USA.
8University of Colorado Anschutz Medical Campus, Department of Surgery.
9University of Georgia College of Pharmacy, Department of Clinical and Administrative Pharmacy, Augusta, GA, USA.
PubMed PMC DOI PDF

Abstract

BACKGROUND: Drug-drug interactions (DDIs) are a significant source of morbidity and adverse drug events (ADEs), particularly in situations of polypharmacy and complex medication regimens. While rules-based software integrated in electronic health records (EHRs) has demonstrated proficiency in identifying DDIs present in medication regimens, large language model (LLM) based identification requires…

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