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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Improving TCM question answering through tree-organized self-reflective retrieval with LLMs.

Chang Liu1,2,3, Ying Chang1, Jianmin Li3

  • 1School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.

Frontiers in Medicine
|March 30, 2026
PubMed
Summary
This summary is machine-generated.

A new Tree-Organized Self-Reflective Retrieval (TOSRR) framework significantly improves large language model (LLM) accuracy for Traditional Chinese Medicine (TCM) question answering. This AI approach enhances knowledge organization and retrieval for better medical education and practice.

Keywords:
Traditional Chinese Medicineartificial intelligenceknowledge graphlarge language modelmedical dialogue system

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Area of Science:

  • Artificial Intelligence in Medicine
  • Knowledge Representation
  • Natural Language Processing

Background:

  • Large language models (LLMs) show promise for healthcare question answering (Q&A).
  • Traditional knowledge representation struggles with the complexity of Traditional Chinese Medicine (TCM).
  • Existing retrieval-augmented generation (RAG) frameworks are not optimized for TCM's unique structure.

Purpose of the Study:

  • To evaluate a novel Tree-Organized Self-Reflective Retrieval (TOSRR) framework.
  • To enhance LLM performance on TCM Q&A tasks using innovative knowledge organization and self-correction.

Main Methods:

  • Developed a hierarchical knowledge system structuring TCM as subject-predicate-object-text (SPO-T) units.
  • Implemented an iterative self-reflection mechanism for dynamic knowledge retrieval and validation.
  • Evaluated performance using TCM Medical Licensing Examination (MLE) and Classics Course Exam (CCE) questions.

Main Results:

  • TOSRR integrated with GPT-4 improved TCM MLE accuracy by 19.85% and CCE recall from 27% to 38%.
  • Expert evaluation showed an 18.64-point improvement in safety, consistency, explainability, compliance, and coherence.
  • Retrieval-Augmented Generation Assessment (RAGAs) metrics confirmed superior knowledge utilization and precision.

Conclusions:

  • The TOSRR framework effectively enhances LLM performance in TCM knowledge tasks.
  • Hierarchical knowledge representation and self-reflective retrieval are key to its success.
  • The framework shows potential for application in TCM education and teaching.