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

TL;DR

DiagLink is a dual-user diagnostic system that combines LLMs, knowledge graphs, and medical experts to enhance diagnostic accuracy, trust, and usability for both patients and physicians.

Contribution

This work introduces DiagLink, a novel system integrating LLMs, knowledge graphs, and expert oversight for dual-user medical diagnosis support.

Findings

Improves user satisfaction and diagnostic efficiency

Enhances trust through physician oversight and dynamic knowledge validation

Demonstrates effectiveness via user studies and expert interviews

Abstract

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.