# OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis

arXiv: https://arxiv.org/abs/2504.20118 · HTML: https://mlsanity.com/paper/2504.20118/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Kimi k1.5: Scaling Reinforcement Learning with LLMs](https://mlsanity.com/paper/2501.12599/) (2025)
- [Graph Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2408.08921/) (2024)
- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue](https://mlsanity.com/paper/2308.03549/) (2023)
- [Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2408.04187/) (2024)
- [A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology](https://mlsanity.com/paper/2308.04709/) (2023)
- [DrHouse: An LLM-empowered Diagnostic Reasoning System through Harnessing Outcomes from Sensor Data and Expert Knowledge](https://mlsanity.com/paper/2405.12541/) (2024)
- [Qibo: A Large Language Model for Traditional Chinese Medicine](https://mlsanity.com/paper/2403.16056/) (2024)
- [BianCang: A Traditional Chinese Medicine Large Language Model](https://mlsanity.com/paper/2411.11027/) (2024)
- [SHADE-AD: An LLM-Based Framework for Synthesizing Activity Data of Alzheimer's Patients](https://mlsanity.com/paper/2503.01768/) (2025)

## Built on by (most cited first, 10 of 10)

- [TCM-Eval: An Expert-Level Dynamic and Extensible Benchmark for Traditional Chinese Medicine](https://mlsanity.com/paper/2511.07148/) (2025)
- [DoPI: Doctor-like Proactive Interrogation LLM for Traditional Chinese Medicine](https://mlsanity.com/paper/2507.04877/) (2025)
- [DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals](https://mlsanity.com/paper/2512.08379/) (2025)
- [TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought](https://mlsanity.com/paper/2602.22828/) (2026)
- [CORE-Acu: Structured Reasoning Traces and Knowledge Graph Safety Verification for Acupuncture Clinical Decision Support](https://mlsanity.com/paper/2603.08321/) (2026)
- [TCMIIES: A Browser-Based LLM-Powered Intelligent Information Extraction System for Academic Literature](https://mlsanity.com/paper/2605.07507/) (2026)
- [DeepRoot: A KG-Coordinated Multi-Agent System for Therapeutic Reasoning over Historical Medical Texts](https://mlsanity.com/paper/2606.15931/) (2026)
- [MMIR-TCM: Memory-Integrated Multimodal Inference and Retrieval for TCM Clinical Decision Support](https://mlsanity.com/paper/2607.01814/) (2026)
- [TCMaster: Confidence-Aware Querying and Workload-Guided Physical Design for Multi-Source Traditional Chinese Medicine Knowledge Graphs](https://mlsanity.com/paper/2609.25712/) (2026)
- [LingLan: An Advancing Traditional Chinese Medicine Diagnosis LLM with Multimodal Data](https://mlsanity.com/paper/2609.25715/) (2026)

## Abstract (arXiv)

Traditional Chinese Medicine (TCM) represents a rich repository of ancient medical knowledge that continues to play an important role in modern healthcare. Due to the complexity and breadth of the TCM literature, the integration of AI technologies is critical for its modernization and broader accessibility. However, this integration poses considerable challenges, including the interpretation of obscure classical Chinese texts and the modeling of intricate semantic relationships among TCM concepts. In this paper, we develop OpenTCM, an LLM-based system that combines a domain-specific TCM knowledge graph and Graph-based Retrieval-Augmented Generation (GraphRAG). First, we extract more than 3.73 million classical Chinese characters from 68 gynecological books in the Chinese Medical Classics Database, with the help of TCM and gynecology experts. Second, we construct a comprehensive multi-relational knowledge graph comprising more than 48,000 entities and 152,000 interrelationships, using customized prompts and Chinese-oriented LLMs such as DeepSeek and Kimi to ensure high-fidelity semantic understanding. Last, we empower OpenTCM with GraphRAG, enabling high-fidelity ingredient knowledge retrieval and diagnostic question-answering without model fine-tuning. Experimental evaluations demonstrate that OpenTCM achieves mean expert scores (MES) of 4.378 in ingredient information retrieval and 4.045 in diagnostic question-answering tasks, outperforming state-of-the-art solutions in real-world TCM use cases.