C-PATH：医疗系统中的对话式患者协助与分诊
C-PATH: Conversational Patient Assistance and Triage in Healthcare System
AI总结：
本文提出基于 LLaMA3 的对话式医疗分诊系统 C-PATH，利用 GPT 数据增强和历史管理提升症状识别与科室推荐效果。
摘要：
在医疗系统中导航可能复杂且令人不知所措，给寻求及时、适当医疗照护的患者造成障碍。本文介绍 C-PATH（Conversational Patient Assistance and Triage in Healthcare，医疗中的对话式患者协助与分诊），这是一种由大型语言模型（LLM）驱动的新型对话式 AI 系统，旨在通过自然的多轮对话帮助患者识别症状并推荐合适的医疗科室。C-PATH 基于 LLaMA3 架构构建的多阶段流程，在医学知识、对话数据和临床摘要上进行了微调。本工作的一项核心贡献是一个基于 GPT 的数据增强框架，该框架将来自 DDXPlus 的结构化临床知识转化为便于非专业人士理解的对话，从而与患者沟通规范保持一致。我们还实现了一种可扩展的对话历史管理策略，以确保长程连贯性。使用 GPTScore 进行的评估表明，系统在清晰度、信息性和推荐准确性等维度上表现出色。定量基准测试显示，C-PATH 在由 GPT 改写的对话数据集上取得了优越性能，显著优于特定领域基线。C-PATH 代表了在开发以用户为中心、可访问且准确的数字健康协助与分诊 AI 工具方面迈出的一步。
英文摘要：
Navigating healthcare systems can be complex and overwhelming, creating barriers for patients seeking timely and appropriate medical attention. In this paper, we introduce C-PATH (Conversational Patient Assistance and Triage in Healthcare), a novel conversational AI system powered by large language models (LLMs) designed to assist patients in recognizing symptoms and recommending appropriate medical departments through natural, multi-turn dialogues. C-PATH is fine-tuned on medical knowledge, dialogue data, and clinical summaries using a multi-stage pipeline built on the LLaMA3 architecture. A core contribution of this work is a GPT-based data augmentation framework that transforms structured clinical knowledge from DDXPlus into lay-person-friendly conversations, allowing alignment with patient communication norms. We also implement a scalable conversation history management strategy to ensure long-range coherence. Evaluation with GPTScore demonstrates strong performance across dimensions such as clarity, informativeness, and recommendation accuracy. Quantitative benchmarks show that C-PATH achieves superior performance in GPT-rewritten conversational datasets, significantly outperforming domain-specific baselines. C-PATH represents a step forward in the development of user-centric, accessible, and accurate AI tools for digital health assistance and triage.
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。
