Rare disease diagnosis is inherently challenging due to heterogeneous symptoms, limited clinical familiarity, and fragmented evidence across specialties. Recent large language model (LLM)-based agentic systems have shown promise by simulating multidisciplinary team discussions to generate and evaluate diagnostic hypotheses. However, fully automated diagnosis remains unrealistic, and existing human-in-the-loop approaches provide limited support for effective clinician-agent collaboration. In practice, clinicians are often presented with final diagnostic outputs and lengthy, unstructured agent discussion logs, making it difficult to inspect reasoning, intervene in a timely manner, or guide agent deliberation effectively. To address these challenges, we developed MDTRoom, an interactive system that transforms multi-agent discussions from linear transcripts into a structured, inspectable workspace. The system externalizes patient data, evidence provenance, hypothesis evolution, and inter-agent conflicts as interconnected visual objects, enabling clinicians to efficiently examine, intervene in, and guide agent reasoning. Our evaluation demonstrates the effectiveness of MDTRoom in supporting clinician-agent collaboration.

翻译：罕见病诊断因症状异质性高、临床认知有限及专科证据碎片化而极具挑战。基于大语言模型的智能体系统通过模拟多学科团队讨论生成并评估诊断假设，已展现出潜力。然而，全自动化诊断仍不现实，现有的人机协同方法对实现有效临床专家-智能体协作支撑不足。实践中，临床医生往往仅能看到最终诊断输出及冗长非结构化的智能体讨论记录，难以审查推理过程、及时介入或有效引导智能体决策。针对上述挑战，我们开发了MDTRoom交互系统，将多智能体讨论从线性文本转化为结构化、可审查的工作空间。该系统将患者数据、证据溯源、假设演化及智能体间冲突等要素外化为可交互可视化对象，使临床医生能够高效审查、干预并引导智能体推理过程。实验评估表明，MDTRoom在支撑临床专家-智能体协作方面具有显著有效性。