用于传达坏消息的人工智能驱动虚拟患者：面部表情强度的专家形成性研究
An AI-Driven Virtual Patient for Breaking Bad News: An Expert Formative Study on Facial Expression Intensity
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中文总结 AI 辅助
研究探讨大语言模型驱动的虚拟患者情感表达设计难题，提出结合大语言模型对话与实时面部动画的框架，通过对七位医学专家评估，发现专家通过多渠道评估情感真实感，为未来医学培训模拟器提出需同步多模态管道的路线图。
中文摘要 AI 辅助
由大语言模型驱动的交互式虚拟患者为医疗沟通培训提供了可扩展的解决方案，如传达坏消息。然而，设计其情感表达仍是一项挑战。本文提出了一个人工智能驱动的虚拟患者框架，将大语言模型对话与虚拟现实中的实时面部动画相结合。我们对七位医学专家进行了探索性的形成性评估，以收集早期反馈并引出设计要求。评估聚焦于面部表情强度的变化如何影响感知的真实感和虚拟患者的情感清晰度。虽然描述性定量评分在各条件下保持在基线水平，但定性访谈深入洞察了专家如何感知虚拟情感线索。研究结果表明，专家通过多种言语和非言语渠道整体评估情感真实感；孤立的面部调整很容易被对话细微差别和语音韵律掩盖。基于这些见解，我们提出了未来医学培训模拟器的路线图，强调了整合身体姿势、对话停顿和语音动态的同步多模态管道的必要性。
英文摘要
Interactive virtual patients driven by large language models (LLMs) offer scalable solutions for medical communication training, such as breaking bad news. However, designing their emotional expressiveness remains a challenge. This paper presents an AI-driven virtual patient framework combining LLM dialogue with real-time facial animation in virtual reality (VR). We conducted an exploratory, formative evaluation with seven medical experts to gather early feedback and elicit design requirements. The evaluation focused on how variations in facial expression intensity affect perceived realism and the virtual patient's emotion intelligibility. While descriptive quantitative ratings remained baseline across conditions, qualitative interviews provided deep insights into how experts perceive virtual emotional cues. The findings suggest that experts evaluate emotional realism holistically through multiple verbal and non-verbal channels; isolated facial adjustments are easily overshadowed by dialogue nuances and vocal prosody. Based on these insights, we present a roadmap for future medical training simulators, highlighting the need for synchronized, multi-modal pipelines incorporating body gestures, conversational pauses, and vocal dynamics.
