提出去中心化医学多智能体框架，实现肿瘤诊疗决策的隐私保护与可解释协作。
CoMMa: Contribution-Aware Medical Multi-Agents for Decentralized Oncology Decision Support
- 各专科智能体在分割数据流上独立运行，通过微调强化角色专长。
- 引入贡献感知聚合机制，用确定性嵌入投影替代随机推理，实现显式责任分配。
- 在北美与东亚多家医院真实数据集上验证，跨场景泛化能力强，适合临床部署。
现有医学多智能体框架虽具潜力，但通常依赖集中式数据访问并基于提示进行任务分配，在隐私敏感的临床环境中应用受限。本文提出贡献感知医学多智能体系统（CoMMa），一种去中心化的LLM-智能体框架，使各专科智能体在分割的临床数据流上独立运作。不同于以往共享输入的方案，CoMMa强制实现数据去中心化，增强角色专精，并通过智能体特异性微调进一步优化。为确保可靠且可解释的协同，引入贡献感知聚合机制，以确定性嵌入投影替代传统的随机、叙事式推理，近似各智能体的边际贡献。该机制实现对智能体的显式信用分配，生成稳定且符合临床需求的决策路径。我们在多个肿瘤学基准上评估CoMMa，涵盖来自北美和东亚大型学术医院的真实多学科肿瘤委员会数据集以及公开数据集，结果表明其在异构临床环境下表现出色，具备强性能与良好泛化能力。
原文摘要 · Abstract (English)
Recent multi-agent frameworks have shown promise for oncology decision support, yet most assume centralized data access and rely on prompt-based assignment, limiting their applicability in privacy-sensitive clinical settings. We propose Contribution-Aware Medical Multi-Agents (CoMMa), a decentralized LLM-agent framework where specialists operate on partitioned clinical data streams. Unlike prior approaches that share inputs across agents, CoMMa enforces data decentralization to include stronger role specialization and further enhances this via agent-specific finetuning. To enable reliable and interpretable coordination, we introduce a contribution-aware aggregation mechanism that replaces stochastic, narrative-based reasoning with deterministic embedding projections to approximate each agent's marginal utility. This yields explicit credit assignment over agents, providing a stable and interpretable decision pathway aligned with clinical requirements. We evaluate CoMMa on multiple oncology benchmarks, including real-world multidisciplinary tumor board datasets from large academic hospitals in North America and East Asia, as well as public datasets, demonstrating strong performance and generalization across heterogeneous clinical settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。