Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generation
Quick Answer
This study presents a knowledge-enhanced visual diagnostic system for traditional Chinese medicine, utilizing a Neo4j knowledge graph with 241 syndromes and 1,263 symptoms.
Quick Take
The system improved diagnostic trust by 1.82 standard deviations and reduced non-standard outputs by 32%, enhancing transparency and interpretability in treatment planning.
Key Points
- The system includes a four-stage symptom matching pipeline and proactive questioning strategy.
- Knowledge graph constraints reduced non-standard outputs by 32%.
- Case studies showed significant improvements in diagnostic trust and reduced cognitive load.
- Automated evaluations indicated higher credibility of evidence-based references.
- The system enhances interpretability of treatment plans in traditional Chinese medicine.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 06869v1 Announce Type: new Abstract: Aim: Existing AI-assisted traditional Chinese medicine diagnostic tools suffer from opaque reasoning processes, passive interaction, and limited treatment plan presentation. This study proposes a knowledge-enhanced visual diagnostic system to improve the transparency and interpretability of syndrome differentiation and treatment. Methods: The system is built upon a Neo4j knowledge graph comprising 241 syndromes, 1,263 symptoms, and 2,485 relations.
It incorporates a four-stage symptom matching pipeline (exact, semantic, fuzzy, and verification), an information gain-driven proactive questioning strategy optimized with genetic algorithms, and a multimodal treatment presentation integrating artificial intelligence-generated illustrations, three-dimensional meridian-acupoint models, and evidence-based literature. Results: Knowledge graph constraints reduced non-standard outputs by 32%.
Case studies validated the effectiveness of the interactive workflow across patient self-assessment, clinician-assisted diagnosis, and traditional Chinese medicine education. Automated paired-comparison evaluation across 30 cases further demonstrated significant improvements in diagnostic trust (Cohen's d = 1. 82, p < 0. 001), reduced cognitive load (improvements in four of five dimensions), and higher credibility of evidence-based references (4. 21 vs. 2. 95).
Conclusions: The proposed system enhances the transparency of traditional Chinese medicine diagnostic reasoning and the interpretability of treatment plans through knowledge graph-driven visualization and multimodal interaction, offering a practical solution for trustworthy artificial intelligence-assisted traditional Chinese medicine applications.
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