Healthcare Copilot: Eliciting the Power of General LLMs for Medical
  Consultation
Zhiyao Ren Yibing Zhan Baosheng Yu Liang Ding Dacheng Tao
The copilot framework, which aims to enhance and tailor large language models
(LLMs) for specific complex tasks without requiring fine-tuning, is gaining
increasing attention from the community. In this paper, we introduce the
construction of a Healthcare Copilot designed for medical consultation. The
proposed Healthcare Copilot comprises three main components: 1) the Dialogue
component, responsible for effective and safe patient interactions; 2) the
Memory component, storing both current conversation data and historical patient
information; and 3) the Processing component, summarizing the entire dialogue
and generating reports. To evaluate the proposed Healthcare Copilot, we
implement an auto-evaluation scheme using ChatGPT for two roles: as a virtual
patient engaging in dialogue with the copilot, and as an evaluator to assess
the quality of the dialogue. Extensive results demonstrate that the proposed
Healthcare Copilot significantly enhances the capabilities of general LLMs for
medical consultations in terms of inquiry capability, conversational fluency,
response accuracy, and safety. Furthermore, we conduct ablation studies to
highlight the contribution of each individual module in the Healthcare Copilot.
Code will be made publicly available on GitHub.