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Published on in Vol 13 (2025)

This is a member publication of

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75279, first published .
Edible insects, spices, and ingredients on a slate board, ready for cooking.

Leveraging Retrieval-Augmented Large Language Models for Dietary Recommendations With Traditional Chinese Medicine’s Medicine Food Homology: Algorithm Development and Validation

Leveraging Retrieval-Augmented Large Language Models for Dietary Recommendations With Traditional Chinese Medicine’s Medicine Food Homology: Algorithm Development and Validation

Journals

  1. Li X, He H, Lu G, Yue P, Chen J, Yang Z, Hon C. TCM-DS: a large language model for intelligent traditional Chinese medicine edible herbal formulas recommendations. Chinese Medicine 2025;20(1) View
  2. Sun D, Chen P, Tao L, Ma P, Meng L, Yin S, Zhang B, Li S. Network pharmacology in food-medicine homology: AI-driven decoding of multi-target synergy from molecular networks to precision health. Acupuncture and Herbal Medicine 2026;6(1):10 View
  3. Chen J, Luo M, Chen J, Luo G, Li G, Lei C, Chen D, Yu J, Gu K. Construction and evaluation of the knowledge graph and large model question-answering system for Jin San Zhen therapy: a tool study for primary care and general practice. Frontiers in Medicine 2026;13 View
  4. Tseng M, Chen Y, Chen W. A Generative AI Architecture Integrating Retrieval-Augmented Generation and Low-Rank Adaptation for Knowledge-Intensive Medical Reasoning. Future Internet 2026;18(6):280 View

Conference Proceedings

  1. Vavken M, Ogrinc M, Eftimov T, Seljak B. 2025 IEEE International Conference on Big Data (BigData). Evaluation of LLMs in Retrieving Food and Nutritional Context for RAG Systems View
  2. Zhang J, Wang X, Cheng H, Yang Z, Gao H, Zhou C, Huang J, Wang S. 2026 International Conference on Generative Artificial Intelligence and Information Security (GAIIS). A Hybrid Evaluation Architecture for Electronic Components Selection Integrating Large Language Models and Retrieval-Augmented Generation View