Evaluation of the integration of retrieval-augmented generation in large language model for breast cancer nursing care responses.

ABSTRACT: Breast cancer is one of the most common malignant tumors in women worldwide. Although large language models (LLMs) can provide breast cancer nursing care consultation, inherent hallucinations can lead to inaccurate responses. Retrieval-augmented generation (RAG) technology can improve LLM performance, offering a new approach for clinical applications. In the present study, we evaluated the performance of a LLM in breast cancer nursing care using RAG technology. In the control group (GPT-4), questions were answered directly using the GPT-4 model, whereas the experimental group (RAG-GPT) used the GPT-4 model combined with RAG. A knowledge base for breast cancer nursing was created for the RAG-GPT group, and 15 of 200 real-world clinical care questions were answered randomly. The primary endp

SUBMITTER: Xu R 

PROVIDER: S-EPMC11680762 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature