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English(EN) Decoupling Patient Personas and Clinical Workflows: A Dual-Agent Approach to Realistic Medical Dialogue Generation

新的双代理框架生成真实的医疗对话

研究人员开发了CoRPS-Dial,一个新颖的双代理框架,旨在为就诊前场景生成真实的医疗对话。该方法将患者画像与临床工作流规则分开,从而在合成数据方面实现更大的控制和多样性。该系统包括一个由患者画像驱动的患者代理和一个由临床指南指导的医生代理,并进行事后一致性检查。实验表明,CoRPS-Dial在信息覆盖和临床合规性方面能有效提高合成对话与真实数据的匹配度,证明了其在医疗对话任务中的实用性。 AI

影响 该框架可以显著改善合成医疗数据的创建,有助于在不侵犯患者隐私的情况下训练临床应用的人工智能模型。

排序理由 该集群包含一篇详细介绍医疗对话生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的双代理框架生成真实的医疗对话

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该集群包含一篇详细介绍医疗对话生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Qiyuan Li, Xiaomei Guo, Chao Gao, Haijiang Liu, Deyu Chen, Caicai Guo, Jinguang Gu ·

    解耦患者画像与临床工作流:一种生成逼真医疗对话的双代理方法

    arXiv:2610.32443v1 Announce Type: cross Abstract: Pre-consultation medical dialogues are essential for collecting comprehensive patient information, but are costly to obtain at scale due to privacy and ethical constraints. Existing LLM-based synthesis methods often rely on prompt…