This paper is in the following e-collection/theme issue:

Innovations and Technology for Healthy Eating Education (506) Generative Language Models Including ChatGPT (782) Cardiovascular Disease Prevention (213) Patient Education in Cardiology (50) Machine Learning (2203)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78625, first published .
Evaluating Large Language Models and Retrieval-Augmented Generation Enhancement for Delivering Guideline-Adherent Nutrition Information for Cardiovascular Disease Prevention: Cross-Sectional Study

Evaluating Large Language Models and Retrieval-Augmented Generation Enhancement for Delivering Guideline-Adherent Nutrition Information for Cardiovascular Disease Prevention: Cross-Sectional Study

Evaluating Large Language Models and Retrieval-Augmented Generation Enhancement for Delivering Guideline-Adherent Nutrition Information for Cardiovascular Disease Prevention: Cross-Sectional Study

Authors of this article:

Vijaya Parameswaran1 Author Orcid Image ;   Jenna Bernard2 Author Orcid Image ;   Alec Bernard1 Author Orcid Image ;   Neil Deo3 Author Orcid Image ;   Sean Tsung4 Author Orcid Image ;   Kalle Lyytinen5 Author Orcid Image ;   Christopher Sharp1 Author Orcid Image ;   Fatima Rodriguez1 Author Orcid Image ;   David J Maron1 Author Orcid Image ;   Rajesh Dash1 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Ase A, Borowicz J, Rakocy K, Piekarska B. Large Language Models for Real-World Nutrition Assessment: Structured Prompts, Multi-Model Validation and Expert Oversight. Nutrients 2025;18(1):23 View
  2. ZHANG C, KONG H, YANG Y, YAN Y, TONG T, WANG H. Technical architecture, application progress, and future challenges of nutrition foundation models. Chinese Bulletin of Life Sciences 2026;38(1):1 View