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

Innovations and Technology for Healthy Eating Education (554) Machine Learning (3154) Patient Education in Cardiology (61) Cardiovascular Disease Prevention (268) Generative Language Models Including ChatGPT (1502)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78625, first published .
Overhead view of hands holding a fresh salad bowl surrounded by healthy ingredients like tomatoes, carrots, and lemons.

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 (11) Tweetations 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
  3. Tezcan H, Tunçez A, Gürses K, Özen Y, Yalçın M. Assessing the accuracy and educational value of ChatGPT-generated content for core topics in cardiology: a descriptive analysis at Selçuk University Cardiology Clinic. BMC Medical Education 2026;26(1) View
  4. Dindukurthi V, Jain D, Tripathi A, Obbineni J, Kandasamy I. An explainable graph retrieval augmented generation framework for personalized nutrition recommendation. Frontiers in Artificial Intelligence 2026;9 View
  5. 黄 俊. Evaluation of Mainstream Large Language Models in Health Education on Infusion Port Care: Readability and Quality Analysis. Advances in Clinical Medicine 2026;16(06):2655 View
  6. Massara P, Kirkland J, Pagani I, Huey S, Hirsh H, McDonald D, Patel L, Finkelstein J, Gantz M, Wang F, Erickson D, Wells M, Elemento O, Knight R, Mehta S. Applying Artificial Intelligence and machine learning in precision nutrition. Nature Communications 2026;17(1) View
  7. Chen H, Xiao S, Wan T, Li G, Peng Y, Wang Z. Application of AI in Hypertension Health Education: Scoping Review. Journal of Medical Internet Research 2026;28:e95596 View
  8. Guo Z, Lai A, Korakas E, Vagenas A, Ahamed I, Albor C, Zhang H, Healy J, Li K. Retrieval-Augmented Large Language Model Counseling for Continuous Glucose Monitoring in Diabetes: Source-Masked Multirater Comparative Evaluation. Journal of Medical Internet Research 2026;28:e98519 View
  9. Nakamura K, Shinoda G, Noda A, Ishikuro M, Obara T, Matsubara T, Ishii H, Onishi M, Ohyama Y. Digital twin–supported behavioral intention in mothers of young children to prevent childhood obesity: a large language model–based intervention study. Frontiers in Artificial Intelligence 2026;9 View
  10. Zhao Y, Miao Y, Guo R, Luo Y, Wang H, Wu Y. Evaluation Methods for Inference-Time Retrieval-Augmented and Graph Retrieval-Augmented Large Language Models in Health Care: Scoping Review. Journal of Medical Internet Research 2026;28:e90046 View

Conference Proceedings

  1. Lu H, Wang S, Liu Z. 2026 6th International Conference on Neural Networks, Information and Communication Engineering (NNICE). A Knowledge-Enhanced RAG Framework with Re-ranking and Chain-of-Thought Reasoning for Cardiovascular Disease Question Answering Using Large Language Models View