DiagLink用大模型和知识图谱帮医生患者协同诊断，提升效率与信任。
DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphs
- 双角色对话收集病史，大模型与知识图谱协同推理。
- 用户研究显示诊断效率提升，医生满意度高。
- 适合医疗AI系统设计者及临床辅助工具开发者。
全球医疗专家短缺且分布不均，持续阻碍精准诊断的公平可及。现有智能诊断系统虽有潜力，但大多难以支持双用户交互与动态知识整合，限制了实际应用。本文提出DiagLink，一种融合大语言模型（LLMs）、知识图谱（KGs）与医学专家的双用户诊断辅助系统，支持患者与医生协同工作。DiagLink通过引导式对话获取患者病史，利用LLMs与KGs进行协作推理，并引入医生监督以实现知识持续验证与演化。系统提供角色自适应界面、动态可视化病史及统一多源证据，提升用户信任与可用性。通过用户研究、使用案例和专家访谈评估，证明DiagLink显著提升用户满意度与诊断效率，为未来AI辅助诊断系统设计提供洞见。
原文摘要 · Abstract (English)
The global shortage and uneven distribution of medical expertise continue to hinder equitable access to accurate diagnostic care. While existing intelligent diagnostic system have shown promise, most struggle with dual-user interaction, and dynamic knowledge integration -- limiting their real-world applicability. In this study, we present DiagLink, a dual-user diagnostic assistance system that synergizes large language models (LLMs), knowledge graphs (KGs), and medical experts to support both patients and physicians. DiagLink uses guided dialogues to elicit patient histories, leverages LLMs and KGs for collaborative reasoning, and incorporates physician oversight for continuous knowledge validation and evolution. The system provides a role-adaptive interface, dynamically visualized history, and unified multi-source evidence to improve both trust and usability. We evaluate DiagLink through user study, use cases and expert interviews, demonstrating its effectiveness in improving user satisfaction and diagnostic efficiency, while offering insights for the design of future AI-assisted diagnostic systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。