RareDASH: A Dynamic Multi-Agent System for Holistic Rare Disease Care

RareDASH: A Dynamic Multi-Agent System for Holistic Rare Disease Care

Jialun Zhong, Jiayang Yu, Yanzeng Li, Meng Qin, Lei Zou, Yuqian Wang, Ying Zhang, Hanna Li, Liying Yan, Jie Qiao

Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence
Demo Track. Pages 8564-8568. https://doi.org/10.24963/ijcai.2026/1004

Rare diseases are characterized by low prevalence and intricate pathogenesis, leading to highly heterogeneous clinical trajectories. The care of rare disease presents formidable challenges due to the requirement for highly specialized expertise and experiences. Existing methods are typically tailored for isolated rare disease scenarios (e.g., diagnostic tasks, medication recommendations), which lacks a comprehensive perspective of the entire care process. Inspired by recent studies of agent skills, we propose RareDASH, a multi-agent system (MAS) featuring dynamic workflow orchestration designed to provide a comprehensive solution for the full life-cycle of rare disease care. Our framework is inherently patient-centric, enhancing rare disease discovery capabilities through proactive inquiry and information elicitation directly from the patients. Furthermore, we implement diverse agent memory to optimize both the accuracy and efficiency of the multi-agent collaboration. Finally, an online auditing module is integrated into the system to monitor and mitigate the hallucinations, ensuring the reliability of clinical outputs. The work sheds light on the feasibility of leveraging MAS in holistic rare disease care.
Keywords:
AI: Agent-based and Multi-agent Systems
AI: Planning and Scheduling
AI: Humans and AI