用知识图谱和大模型打造可解释的中医诊断系统，支持多轮互动和可视化治疗方案。
Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generation
- 基于241种证候的知识图谱，四阶段匹配症状并主动提问。
- 诊断可信度提升显著，认知负荷降低，证据引用更可信。
- 适合中医临床、教育及需要透明AI决策的场景。
现有中医辅助诊断工具存在推理过程不透明、交互被动、治疗方案展示有限等问题。本研究提出一种增强知识的可视化诊断系统，以提升证候辨识的透明度与可解释性。系统基于包含241种证候、1,263个症状和2,485条关系的Neo4j知识图谱，采用四阶段症状匹配流程（精确、语义、模糊及大语言模型验证），结合遗传算法优化的信息增益驱动主动提问策略，并融合AI生成插图、三维经络穴位模型及循证文献的多模态治疗呈现。知识图谱约束使非标准输出减少32%。案例研究验证了该交互流程在患者自评、医师辅助诊断及中医教学中的有效性。30例自动配对评估显示，诊断信任度显著提升（Cohen's d = 1.82, p < 0.001），认知负荷在五个维度中有四个得到改善，证据引用可信度从2.95升至4.21。结论：该系统通过知识图谱驱动的可视化与多模态交互，增强了中医诊断推理的透明性与治疗方案的可解释性，为可信的AI辅助中医应用提供可行方案。
原文摘要 · Abstract (English)
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 large language model 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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