Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generation

📄 arXiv: 2606.06869v1 📥 PDF

作者: Yunhan Wang, Yuda Wang, Zhiying Tu, Mingqiang Song, Li Song, Kun Li, Dianhui Chu, Bolin Zhang

分类: cs.AI

发布日期: 2026-06-05

备注: 29 pages, 9 figures, 5 tables, including supporting information

💡 一句话要点

提出知识增强的可视化诊断系统以解决中医诊断透明性不足问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 中医诊断 知识图谱 多模态交互 主动提问 人工智能 可视化系统 透明性 治疗方案

📋 核心要点

  1. 现有的中医诊断工具存在推理过程不透明、互动性差和治疗方案展示有限等问题。
  2. 本研究提出了一种基于知识图谱的可视化诊断系统，结合多模态交互和主动提问策略，提升诊断的透明性和可解释性。
  3. 实验结果显示，该系统在30个案例中显著提高了诊断信任度（Cohen's d = 1.82, p < 0.001），并降低了认知负担。

📝 摘要（中文）

本研究旨在改善现有中医诊断工具的透明性和可解释性，提出了一种基于知识图谱的可视化诊断系统。该系统基于包含241种症状、1263种症状和2485种关系的Neo4j知识图谱，采用四阶段症状匹配流程和信息增益驱动的主动提问策略。实验结果表明，该系统显著提高了诊断信任度，降低了认知负担，并提升了基于证据的参考文献的可信度，提供了一个可靠的中医人工智能辅助应用解决方案。

🔬 方法详解

问题定义：本研究旨在解决现有中医诊断工具推理过程不透明和互动性不足的问题，导致用户对诊断结果的信任度降低。

核心思路：通过构建知识图谱和多模态交互，提升中医诊断的透明性和可解释性，使用户能够更好地理解诊断过程和治疗方案。

技术框架：系统由四个主要模块组成：症状匹配管道（包括精确匹配、语义匹配、模糊匹配和大语言模型验证）、主动提问策略、知识图谱约束和多模态治疗方案展示。

关键创新：最重要的创新在于结合知识图谱与主动提问策略，显著减少了非标准输出，并通过多模态展示提升了用户体验。

关键设计：系统采用遗传算法优化的主动提问策略，确保信息增益最大化；同时，利用AI生成的插图和三维经络腧穴模型增强治疗方案的可视化效果。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，知识图谱约束使非标准输出减少32%，在30个案例中的自动配对比较评估中，诊断信任度显著提高（Cohen's d = 1.82, p < 0.001），认知负担在五个维度中有四个维度得到改善，基于证据的参考文献可信度提升至4.21。

🎯 应用场景

该研究的潜在应用领域包括中医教育、临床诊断和患者自我评估等。通过提升中医诊断的透明性和可解释性，能够增强患者对中医治疗的信任，推动中医与现代医学的结合，具有重要的实际价值和未来影响。

📄 摘要（原文）

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.