医疗副驾驶：激发通用大语言模型在医疗咨询中的能力
Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultation
AI总结：
本文提出医疗副驾驶框架，通过对话、记忆和处理三个组件增强通用大语言模型在医疗咨询中的询问、流畅性、准确性与安全性，并利用ChatGPT自动评估验证其有效性。
摘要：
副驾驶框架旨在无需微调的情况下增强和定制大语言模型（LLMs）以完成特定复杂任务，正日益受到社区的关注。本文介绍了面向医疗咨询的医疗副驾驶（Healthcare Copilot）的构建。所提出的医疗副驾驶包含三个主要组件：1）对话组件，负责有效且安全的患者交互；2）记忆组件，存储当前对话数据和历史患者信息；3）处理组件，总结整个对话并生成报告。为评估所提出的医疗副驾驶，我们实现了一种使用ChatGPT的自动评估方案，让其扮演两个角色：作为虚拟患者与副驾驶进行对话，以及作为评估者评估对话质量。大量结果表明，所提出的医疗副驾驶在询问能力、对话流畅性、回答准确性和安全性方面显著增强了通用大语言模型在医疗咨询中的能力。此外，我们进行了消融研究，以突出医疗副驾驶中每个单独模块的贡献。代码将在GitHub上公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
