Nursing Retrieval-Augmented Generation: Retrieval augmented generation for nursing question answering with large language models.

ABSTRACT:

Objective

This study aimed to develop a Nursing Retrieval-Augmented Generation (NurRAG) system based on large language models (LLMs) and to evaluate its accuracy and clinical applicability in nursing question answering.

Methods

A multidisciplinary team consisting of nursing experts, artificial intelligence researchers, and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation. The system included four functional modules: 1) construction of a nursing knowledge base through document normalization, embedding, and vector indexing; 2) nursing question filtering using a supervised classifier; 3) semantic retrieval and re-ranking for evidence selection; and 4) evidence-conditioned language model generation to

SUBMITTER: Xiong L 

PROVIDER: S-EPMC12684753 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature