Evaluating Retrieval-Augmented Generation-Large Language Models for Infective Endocarditis Prophylaxis: Clinical Accuracy and Efficiency.

ABSTRACT:

Introduction and aims

The use of large language models (LLMs) in healthcare is expanding. Retrieval-augmented generation (RAG) addresses key LLM limitations by grounding responses in domain-specific, up-to-date information. This study evaluated RAG-augmented LLMs for infective endocarditis (IE) prophylaxis in dental procedures, comparing their performance with non-RAG models assessed in our previous publication using the same question set. A pilot study also explored the utility of an LLM as a clinical decision support tool.

Methods

An established IE prophylaxis question set from previous research was used to ensure comparability. Ten LLMs integrated with RAG were tested using MiniLM L6 v2 embeddings and FAISS to retrieve relevant content from the 2021 American Heart Associa

SUBMITTER: Rewthamrongsris P 

PROVIDER: S-EPMC12828207 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature