MedLatentDx: Latent Multi-Agent Communication for Cross-Hospital Rare-Disease Diagnosis
Quick Answer
MedLatentDx introduces a latent multi-agent communication framework for rare disease diagnosis across hospitals, improving diagnostic performance on the CrossRare-Bench benchmark while minimizing reconstructable clinical content.
Quick Take
MedLatentDx introduces a latent multi-agent communication framework for rare disease diagnosis across hospitals, improving diagnostic performance on the CrossRare-Bench benchmark while minimizing reconstructable clinical content. This approach allows hospitals to maintain privacy by sending only compact latent KV blocks instead of identifiable clinical text.
Key Points
- Rare diseases affect over 300 million patients globally across 7,000 conditions.
- MedLatentDx uses latent KV distillation for same-backbone hospital agents.
- Cross-family latent alignment is utilized for hospitals with different LLM backbones.
- The framework reduces reconstructable clinical content compared to raw-latent communication.
- CrossRare-Bench serves as a large-scale benchmark for evaluating diagnostic performance.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 13945v1 Announce Type: new Abstract: 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.
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