用大模型自动生成医疗对话脚本，确保内容安全可编辑。
HealthDial: A No-Code LLM-Assisted Dialogue Authoring Tool for Healthcare Virtual Agents
- 输入健康教育文本，自动构建分阶段对话框架
- 输出有限状态机，杜绝幻觉风险，保障内容安全
- 无代码界面支持人工优化，适合医疗从业者使用
我们提出HealthDial，一款面向医疗从业者和教育者的对话创作工具，用于构建多轮交互的虚拟健康助手。该工具利用大语言模型（LLMs），基于文本型患者健康教育材料，自动生成每轮会话的结构化对话内容。生成的对话以有限状态机形式输出，便于验证，避免因大模型幻觉导致不安全建议。作者可在无代码界面中编辑对话结构与语言，确保内容准确性、清晰度与传播效果。通过针对癌症筛查教育的可行性与可用性研究，参与者使用HealthDial创作对话后，再与3D动画虚拟助手进行交互测试。基于对任务体验及最终对话的评估，结果表明HealthDial为咨询师提供了一种高效路径，可全面覆盖健康教育内容，同时生成清晰、可操作的患者对话。
原文摘要 · Abstract (English)
We introduce HealthDial, a dialogue authoring tool that helps healthcare providers and educators create virtual agents that deliver health education and counseling to patients over multiple conversations. HealthDial leverages large language models (LLMs) to automatically create an initial session-based plan and conversations for each session using text-based patient health education materials as input. Authored dialogue is output in the form of finite state machines for virtual agent delivery so that all content can be validated and no unsafe advice is provided resulting from LLM hallucinations. LLM-drafted dialogue structure and language can be edited by the author in a no-code user interface to ensure validity and optimize clarity and impact. We conducted a feasibility and usability study with counselors and students to test our approach with an authoring task for cancer screening education. Participants used HealthDial and then tested their resulting dialogue by interacting with a 3D-animated virtual agent delivering the dialogue. Through participants' evaluations of the task experience and final dialogues, we show that HealthDial provides a promising first step for counselors to ensure full coverage of their health education materials, while creating understandable and actionable virtual agent dialogue with patients.
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