Rare diseases affect over $300$ million patients across more than $7{,}000$ conditions, yet no single hospital encounters enough cases of any one condition for reliable diagnosis. Cross-hospital collaboration could help by allowing a diagnosing institution to use distributed, case-specific diagnostic evidence, but privacy regulations restrict the transmission of identifiable clinical text across institutional boundaries. This setting raises two challenges: existing medical agent systems often rely on textual evidence exchange, while raw latent states such as hidden states and KV caches may still reveal prompt-derived clinical content. We introduce MedLatentDx, a latent multi-agent communication framework in which hospital agents keep private clinical records and retrieved cases local, and send compact latent KV blocks to a host agent for rare-disease diagnosis. MedLatentDx supports two deployment settings: same-backbone hospital agents use latent KV distillation, while hospitals with different LLM backbones use cross-family latent alignment. On CrossRare-Bench, a self-built large-scale rare-disease benchmark with hospital-level partitions, MedLatentDx improves cross-hospital diagnostic performance while reducing reconstructable clinical content relative to raw-latent communication baselines.

翻译：[译] 罕见病涉及超过7000种疾病，影响逾3亿患者，但任何单一医院都难以积累足够病例以实现可靠诊断。跨医院协作虽能通过共享分布式病例特异性诊断证据提供助力，但隐私法规限制了可识别临床文本的跨机构传输。该场景面临双重挑战：现有医疗智能体系统多依赖文本证据交换，而原始隐状态（如隐藏状态和KV缓存）仍可能泄露提示衍生的临床内容。我们提出MedLatentDx——一种隐式多智能体通信框架，其中各医院智能体将私有临床记录与检索病例保留在本地，仅向宿主智能体发送紧凑的隐式KV模块用于罕见病诊断。该框架支持两种部署场景：基于相同骨干模型的医院智能体采用隐式KV蒸馏，而异构LLM骨干的医院则通过跨家族隐式对齐实现协作。在自建的大规模罕见病基准测试集CrossRare-Bench（含医院层级数据划分）上，MedLatentDx在提升跨院诊断性能的同时，相较原始隐状态通信基线显著降低了可重构的临床内容。