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DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphs

arXiv 2601.20311 ↗First posted 28 Jan 2026cs.HC cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 106 references that are in the map. First posted on arXiv 28 Jan 2026.

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30 of 106 references.

References · 30

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18 more
  1. 2024KNOWNET: Guided Health Information Seeking from LLMs via Knowledge Graph Integrationcited by 15
  2. 2025Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gapcited by 13
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