Effective patient communication is pivotal in healthcare, yet traditional medical training often lacks exposure to diverse, challenging interpersonal dynamics. To bridge this gap, this study proposes the use of Large Language Models (LLMs) to simulate authentic patient communication styles, specifically the "accuser" and "rationalizer" personas derived from the Satir model, while also ensuring multilingual applicability to accommodate diverse cultural contexts and enhance accessibility for medical professionals. Leveraging advanced prompt engineering, including behavioral prompts, author's notes, and stubbornness mechanisms, we developed virtual patients (VPs) that embody nuanced emotional and conversational traits. Medical professionals evaluated these VPs, rating their authenticity (accuser: $3.8 \pm 1.0$; rationalizer: $3.7 \pm 0.8$ on a 5-point Likert scale (from one to five)) and correctly identifying their styles. Emotion analysis revealed distinct profiles: the accuser exhibited pain, anger, and distress, while the rationalizer displayed contemplation and calmness, aligning with predefined, detailed patient description including medical history. Sentiment scores (on a scale from zero to nine) further validated these differences in the communication styles, with the accuser adopting negative ($3.1 \pm 0.6$) and the rationalizer more neutral ($4.0 \pm 0.4$) tone. These results underscore LLMs' capability to replicate complex communication styles, offering transformative potential for medical education. This approach equips trainees to navigate challenging clinical scenarios by providing realistic, adaptable patient interactions, enhancing empathy and diagnostic acumen. Our findings advocate for AI-driven tools as scalable, cost-effective solutions to cultivate nuanced communication skills, setting a foundation for future innovations in healthcare training.

翻译：有效的患者沟通在医疗保健中至关重要，然而传统医学培训往往缺乏对多样化、具有挑战性人际动态的接触。为弥合这一差距，本研究提出利用大型语言模型（LLMs）模拟真实的患者沟通风格，特别是基于萨提亚模型衍生的“指责者”与“合理化者”人格类型，同时确保多语言适用性以适应多元文化背景并提升医疗专业人员的可及性。通过运用包括行为提示、作者注记及固执性机制在内的高级提示工程，我们开发了体现细腻情感与会话特征的虚拟患者（VPs）。医疗专业人员对这些虚拟患者进行了评估，对其真实性（指责者：$3.8 \pm 1.0$；合理化者：$3.7 \pm 0.8$，采用五点李克特量表（从一到五））进行了评分，并准确识别了其沟通风格。情感分析揭示了不同的特征：指责者表现出痛苦、愤怒和困扰，而合理化者则呈现沉思与平静，这与预先定义的、包含病史的详细患者描述相符。情感得分（范围从零到九）进一步验证了这些沟通风格的差异，指责者采用负面语气（$3.1 \pm 0.6$），而合理化者语气更为中性（$4.0 \pm 0.4$）。这些结果凸显了大型语言模型复制复杂沟通风格的能力，为医学教育提供了变革性潜力。该方法通过提供真实、可适应的患者互动，使受训者能够应对具有挑战性的临床情境，从而增强共情能力与诊断敏锐度。我们的研究主张将人工智能驱动工具作为可扩展、成本效益高的解决方案，以培养细腻的沟通技能，为未来医疗培训的创新奠定基础。