  The patient-centered medical dialogue systems strive to offer diagnostic
interpretation services to users who are less knowledgeable about medical
knowledge, through emphasizing the importance of providing responses specific
to the patients. It is difficult for the large language models (LLMs) to
guarantee the specificity of responses in spite of its promising performance
even in some tasks in medical field. Inspired by in-context learning, we
propose PlugMed, a Plug-and-Play Medical Dialogue System, for addressing this
challenge. PlugMed is equipped with two modules, the prompt generation (PG)
module and the response ranking (RR) module, to enhances LLMs' dialogue
strategies for improving the specificity of the dialogue. The PG module is
designed to stimulate the imitative ability of LLMs by providing them with real
dialogues from similar patients as prompts. The RR module incorporates
fine-tuned small model as response filter to enable the selection of
appropriate responses generated by LLMs. Furthermore, we introduce a new
evaluation method based on matching both user's intent and high-frequency
medical term to effectively assess the specificity of the responses. We conduct
experimental evaluations on three medical dialogue datasets, and the results,
including both automatic and human evaluation, demonstrate the effectiveness of
our approach.
