An Agent-Based Adaptive Medical Dialogue Service for Personalized Healthcare

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.