PubMed Retrieval with RAG Techniques.
Stud Health Technol Inform
; 316: 652-653, 2024 Aug 22.
Article
in En
| MEDLINE
| ID: mdl-39176826
ABSTRACT
This study explores the application of Retriever-Augmented Generation (RAG) in enhancing medical information retrieval from the PubMed database. By integrating RAG with Large Language Models (LLMs), we aim to improve the accuracy and relevance of medical information provided to healthcare professionals. Our evaluation on a labeled dataset of 1,000 queries demonstrates promising results in answer relevance, while highlighting areas for improvement in groundedness and context relevance.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Information Storage and Retrieval
/
PubMed
Limits:
Humans
Language:
En
Journal:
Stud Health Technol Inform
Journal subject:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Year:
2024
Document type:
Article
Affiliation country:
United States
Country of publication:
Netherlands