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.

翻译：全球医疗专业人才的短缺与分布不均持续阻碍着公平获取准确诊断服务的途径。尽管现有的智能诊断系统已展现出潜力，但大多数系统在双用户交互和动态知识整合方面存在困难，这限制了其在现实世界中的适用性。在本研究中，我们提出了DiagLink，一种双用户诊断辅助系统，它协同大型语言模型、知识图谱和医学专家，为患者和医生提供支持。DiagLink利用引导式对话获取患者病史，借助LLM和KG进行协同推理，并纳入医生监督以实现持续的知识验证与演进。该系统提供角色自适应界面、动态可视化的病史记录以及统一的多源证据，以提升信任度和可用性。我们通过用户研究、用例分析和专家访谈对DiagLink进行评估，结果表明其在提高用户满意度和诊断效率方面具有有效性，同时为未来人工智能辅助诊断系统的设计提供了见解。