Enhancement of the Performance of Large Language Models in Diabetes Education through Retrieval-Augmented Generation: Comparative Study.

ABSTRACT:

Background

Large language models (LLMs) demonstrated advanced performance in processing clinical information. However, commercially available LLMs lack specialized medical knowledge and remain susceptible to generating inaccurate information. Given the need for self-management in diabetes, patients commonly seek information online. We introduce the Retrieval-augmented Information System for Enhancement (RISE) framework and evaluate its performance in enhancing LLMs to provide accurate responses to diabetes-related inquiries.

Objective

This study aimed to evaluate the potential of the RISE framework, an information retrieval and augmentation tool, to improve the LLM's performance to accurately and safely respond to diabetes-related inquiries.

Methods

The RISE, an innov

SUBMITTER: Wang D 

PROVIDER: S-EPMC11584532 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature