An Agent-Based Adaptive Medical Dialogue Service for Personalized Healthcare

Fangfang Xu, Pengfei Cheng, Feng Gao, Yinghui Jin, Siyu Yan, Qiao Huang, Yongbo Wang, Xiangyin Ren and Jinguang Gu
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Fangfang Xu: Wuhan University of Science and Technology, China
Pengfei Cheng: Wuhan University of Science and Technology, China
Feng Gao: Wuhan University of Science and Technology, China
Yinghui Jin: Zhongnan Hospital of Wuhan University, China
Siyu Yan: Zhongnan Hospital of Wuhan University, China
Qiao Huang: Zhongnan Hospital of Wuhan University, China
Yongbo Wang: Zhongnan Hospital of Wuhan University, China
Xiangyin Ren: Zhongnan Hospital of Wuhan University, China
Jinguang Gu: Wuhan University of Science and Technology, China

International Journal of Web Services Research (IJWSR), 2025, vol. 22, issue 1, 1-28

Abstract: Large-scale language models have demonstrated robust language understanding and generation capabilities, enabling them to tackle various complex natural language processing tasks. However, for domain-specific tasks like healthcare that require specialized expertise, relying solely on large language models for dialogue generation is insufficient. Moreover, this paper aims to improve the performance of models in medical conversations and enhance the interpretability of the intermediary processes. It argues that leveraging diverse knowledge and agent-based architecture can significantly address the challenges. We introduce an agent-based adaptive medical dialogue service (AMDS) for personalized healthcare. This service utilizes large language models as its cognitive core and integrates medical knowledge extracted from knowledge graph and process knowledge. Extensive experiments show that AMDS outperforms baselines in multi-turn medical dialogue generation tasks.

Date: 2025
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