Drug-drug interaction identification using large language models.

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 thorough benchmarking and performance evaluation using high-quality datasets for safe use. The purpose of this study was to develop a series of performance benchmarking experiments specifically for LLM performance in identification and management of DDIs using a specifically curated clinician-annotated dataset of clinically-relevant DDIs.

Methods

We evaluated three LLMs (GPT-4o-mini,

SUBMITTER: Blotske K 

PROVIDER: S-EPMC12772686 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature