Large language model based health agents are increasingly used by health consumers and clinicians to interpret health information and guide health decisions. However, most AI systems in healthcare operate in siloed configurations, supporting individual users rather than the multi-stakeholder relationships central to healthcare. Such use can fragment understanding and exacerbate misalignment among patients, caregivers, and clinicians. We reframe AI not as a standalone assistant, but as a collaborator embedded within multi-party care interactions. Through a clinically validated fictional pediatric chronic kidney disease case study, we show that breakdowns in adherence stem from fragmented situational awareness and misaligned goals, and that siloed use of general-purpose AI tools does little to address these collaboration gaps. We propose a conceptual framework for designing AI collaborators that surface contextual information, reconcile mental models, and scaffold shared understanding while preserving human decision authority.

翻译：基于大语言模型的健康智能体正越来越多地被健康消费者和临床医生用于解读健康信息及指导健康决策。然而，医疗领域中的大多数人工智能系统以孤立配置运行，仅支持单个用户，而非支持作为医疗核心的多方利益相关者关系。此类使用可能割裂理解，并加剧患者、护理者与临床医生之间的认知错位。我们重新将AI定位为嵌入多方医疗交互中的协作者，而非独立的助手。通过一项经过临床验证的虚构儿童慢性肾病案例研究，我们表明：依从性障碍源于碎片化的情境认知与错位的目标，而孤立使用通用AI工具几乎无法解决这些协作鸿沟。我们提出一个用于设计AI协作者的概念框架，该框架能够揭示情境信息、调和心智模型，并构建共享理解，同时保留人类决策权威。