Comparative evaluation of artificial intelligence platforms and drug interaction screening databases using real-world patient data.

ABSTRACT:

Background

The use of multiple medications increases the risk of harmful drug-drug interactions (DDIs). Conventional DDI screening databases vary in coverage and often trigger low-relevance alerts, contributing to alert fatigue. Large language models (LLMs) have emerged as potential tools for DDI identification, however, their performance compared to established databases using real-world patient data remains under-explored.

Methods

In this exploratory study, we compared conventional database screening with LLM-based screening using anonymized medication lists from rheumatology patients. Lexicomp, Medscape and Drugs.com were used to compile a reference set of 204 clinically relevant interactions across 57 cases. Using identical prompts, we then queried ChatGPT, Google Gemini

SUBMITTER: Domian BM 

PROVIDER: S-EPMC12712589 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature