MedECHO: Privacy-Aware Multi-Agent Collaboration for Cross-Hospital Diagnosis

Medical multi-agent systems combine diagnostic opinions, but their shared-case workflows do not directly fit cross-hospital diagnosis: raw cases remain at the originating hospital, while remote hospitals hold heterogeneous private knowledge. We identify two sequential failures in this setting: restricting disclosure can lose diagnostic signal, and simple voting can miss a correct candidate already returned by collaborators. We propose MEDECHO to address these stages. Controlled Disclosure (CD) builds, reviews, and audits a closed-schema collaboration packet, with local fallback when review fails. Evidence Adoption (EA) gathers source-tagged remote opinions and lets the originating hospital compare candidates against its complete case before issuing the final diagnosis. Extensive experiments on six datasets, three base LLM backbones, and three seeds show that MEDECHO improves dataset-equal accuracy over the strongest evaluated baseline by 15.94, 15.88, and 9.23 percentage points on DeepSeek V4 Flash, Qwen 3.7 Flash, and GPT-5.6-Luna, respectively. These consistent backbone-level gains support the robustness and cross-backbone generalizability of MEDECHO within the evaluated diagnosis setting.