用虚拟患者测试大模型如何说服糖尿病人用闭环胰岛素系统
ChatCLIDS: Simulating Persuasive AI Dialogues to Promote Closed-Loop Insulin Adoption in Type 1 Diabetes Care
- 构建带真实障碍的虚拟患者库，模拟多轮劝导对话
- 大模型虽能调整策略，但在社会压力下仍难突破抗拒
- 为医疗劝导AI提供可复现、高保真的评估平台
一型糖尿病闭环胰岛素系统（CLIDS）在现实中的采纳率低，根源不在于技术问题，而在于行为、心理和社会障碍。我们提出ChatCLIDS，首个用于评估大语言模型（LLM）驱动健康行为改变说服性对话的基准。该框架包含由专家验证的虚拟患者库，每位患者具有临床合理的异质性特征和真实采纳障碍，并模拟护士代理与之进行多轮交互，使用多种基于证据的说服策略。ChatCLIDS支持长期随访式咨询和对抗性社会影响场景，实现多维度评估。结果显示，尽管更大更反思性的LLM能动态调整策略，但所有模型在真实社会压力下均难以克服患者抗拒。这揭示了当前LLM在行为改变中的关键局限，并提供了一个高保真、可扩展的测试平台，推动可信医疗劝导AI的发展。
原文摘要 · Abstract (English)
Real-world adoption of closed-loop insulin delivery systems (CLIDS) in type 1 diabetes remains low, driven not by technical failure, but by diverse behavioral, psychosocial, and social barriers. We introduce ChatCLIDS, the first benchmark to rigorously evaluate LLM-driven persuasive dialogue for health behavior change. Our framework features a library of expert-validated virtual patients, each with clinically grounded, heterogeneous profiles and realistic adoption barriers, and simulates multi-turn interactions with nurse agents equipped with a diverse set of evidence-based persuasive strategies. ChatCLIDS uniquely supports longitudinal counseling and adversarial social influence scenarios, enabling robust, multi-dimensional evaluation. Our findings reveal that while larger and more reflective LLMs adapt strategies over time, all models struggle to overcome resistance, especially under realistic social pressure. These results highlight critical limitations of current LLMs for behavior change, and offer a high-fidelity, scalable testbed for advancing trustworthy persuasive AI in healthcare and beyond.
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