Work

Evaluation of a Retrieval-Augmented Generation–Based Large Language Model for Evidence-Based Herb and Supplement Information in Cancer Care

Year: 2026

Type: article

Abstract: Our study describes the development and evaluation of a retrieval-augmented generation–based large language model to improve the quality of responses to provider questions about herbs and dietary supplements.

Source: JMIR Cancer

DOI: 10.2196/86073

Authors: Yen-Nien Hou, Jyothirmai Gubili, Pulkit Jain, Chun Sing Lam, Avijit Chatterjee, Jun J Mao

Institution: Memorial Sloan Kettering Cancer Center

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,873

Funders: Memorial Sloan-Kettering Cancer Center, National Institutes of Health, National Cancer Institute

Awards: G2051006376, G289723095, G4102770861, Cancer Center Support Grant

OpenAlex ID: W7133757555

FWCI:

Cites: 25

Cited by:

Related to: 10