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References (28)
[3]
OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis
Founder's Pitch
"CORE-Acu: Safeguarding Acupuncture Treatments through Structured Reasoning and Knowledge Graphs for Clinical Decision Support."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
Quick Build
4/4 signals
Series A Potential
2/4 signals
Sources used for this analysis
arXiv Paper
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Analysis model: GPT-4o · Last scored: 4/2/2026
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Why It Matters
This research provides a structured and safe framework for clinical decision support in acupuncture—a field that lacks standardized guidelines and requires interpretability due to its unique and complex nature.
Product Angle
Transform CORE-Acu into a software-as-a-service tool for acupuncture practitioners, offering decision support based on structured reasoning and safety verification from a comprehensive TCM knowledge graph.
Disruption
CORE-Acu replaces less structured and potentially unsafe clinical decision systems or manual decision-making processes that lack clear safety verification and reasoning paths.
Product Opportunity
The market for clinical decision support systems is growing rapidly, especially with increasing integration of traditional medicine practices in modern healthcare. Acupuncture practitioners and integrative medicine centers could benefit by paying subscription fees for more reliable and compliant practice.
Use Case Idea
A clinical decision support tool for acupuncturists that ensures treatments align with Traditional Chinese Medicine principles and safety protocols, allowing practitioners to make informed and safe clinical decisions.
Science
CORE-Acu integrates structured chain-of-thought data and a knowledge graph to offer clear reasoning paths and safety verification in acupuncture treatments. It avoids probabilistic guesses common in LLMs by using a 'Generate-Verify-Revise' loop and specialized loss functions to maintain strict safety compliance and interpretability.
Method & Eval
The system was evaluated across 1,000 cases, showing zero safety violations compared to an 8.5% violation rate in GPT-4o under the same rules, demonstrating exceptional reasoning accuracy and compliance.
Caveats
The niche focus on acupuncture could limit broader adoption unless expanded into other fields of TCM. Also, the dependence on manual dataset annotations and expert validation might raise scalability issues.
Author Intelligence
Liuyi Xu
Northeastern University, Shenyang, China
xuliuyi@mails.neu.edu.cn
Yun Guo
Northeastern University, Shenyang, China
guoyun@mails.neu.edu.cn
Ming Chen
Northeastern University, Shenyang, China
chenm4@mails.neu.edu.cn
Zihan Dun
Yanshan University, Hebei, China
dunzihan@stumail.ysu.edu.cn
Yining Qian
Northeastern University, Shenyang, China
qianyiningning@126.com
An-Yang Lu
Northeastern University, Shenyang, China
luanyang@mail.neu.edu.cn
Shuang Li
Beihang University, Beijing, China
shuangliai@buaa.edu.cn
Lijun Liu
Northeastern University, Shenyang, China
liulijun@mail.neu.edu.cn