Scholarlink

Background <p>Conversational agents are increasingly used in healthcare, but most systems are designed for clinicians and insufficiently support patient-centered decision-making. Limited patient understanding of medical decisions may reduce adherence and lead to doctor-patient conflicts. Existing dialogue systems either lack interpretability or suffer from rigid architectures and limited scalability. This study aims to develop an interpretable, patient-centered medical dialogue system to improve communication and decision support.</p> Methods <p>We propose an intention-oriented multi-agent dialogue system consisting of an intention understanding agent, an argument reasoning agent, and a template-based interaction agent. A medical knowledge ontology was constructed using a hybrid expert-data-driven approach, and a knowledge base was built from recent biomedical literature. The system performs intent recognition, entity extraction, and argument-based reasoning using weighted clinical evidence. A Graves’ eye disease dialogue platform was implemented to evaluate system performance through objective metrics and clinician-based user studies.</p> Results <p>The system achieved an average F1 score of 85% for intent recognition and over 95% response accuracy in evaluations by physicians. User experience assessments showed performance comparable to physician-led online systems and superior to traditional medical Q&amp;A systems. In comparative experiments, the proposed system significantly outperformed ChatGPT in personalized reasoning and interpretability across multiple evaluation dimensions.</p> Conclusions <p>The proposed multi-agent dialogue system improves patient-centered communication by providing interpretable and personalized decision support. It demonstrates strong potential to enhance healthcare dialogue systems and assist clinical decision-making.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An intention-oriented multi-agent dialogue system for patient-centered decision-making

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

查看全文
  • Pengfei Liu,
  • Liang Xiao

摘要

Background

Conversational agents are increasingly used in healthcare, but most systems are designed for clinicians and insufficiently support patient-centered decision-making. Limited patient understanding of medical decisions may reduce adherence and lead to doctor-patient conflicts. Existing dialogue systems either lack interpretability or suffer from rigid architectures and limited scalability. This study aims to develop an interpretable, patient-centered medical dialogue system to improve communication and decision support.

Methods

We propose an intention-oriented multi-agent dialogue system consisting of an intention understanding agent, an argument reasoning agent, and a template-based interaction agent. A medical knowledge ontology was constructed using a hybrid expert-data-driven approach, and a knowledge base was built from recent biomedical literature. The system performs intent recognition, entity extraction, and argument-based reasoning using weighted clinical evidence. A Graves’ eye disease dialogue platform was implemented to evaluate system performance through objective metrics and clinician-based user studies.

Results

The system achieved an average F1 score of 85% for intent recognition and over 95% response accuracy in evaluations by physicians. User experience assessments showed performance comparable to physician-led online systems and superior to traditional medical Q&A systems. In comparative experiments, the proposed system significantly outperformed ChatGPT in personalized reasoning and interpretability across multiple evaluation dimensions.

Conclusions

The proposed multi-agent dialogue system improves patient-centered communication by providing interpretable and personalized decision support. It demonstrates strong potential to enhance healthcare dialogue systems and assist clinical decision-making.