根据病例复杂度动态组队，让不同专家协作诊断更准
One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction
- 按病例难易自动组建专家团队，避免固定角色局限
- 用三值投票机制实现专家主动回避不擅长领域
- 支持可解释决策审计，适合医疗场景高可靠需求
将大语言模型用于临床预测时，简单病例输出一致，复杂病例却对微小提示变化反应剧烈。现有单代理方法仅采样单一角色分布，多代理框架则使用固定角色并采用平票表决，忽略了分歧中的诊断信息。我们提出CAMP（病例自适应多代理审议），由主诊医生代理根据每例病例的诊断不确定性动态组建专科团队。每位专科代理通过三值投票（保留/拒绝/中立）评估候选诊断，实现专业范围外的合理回避。混合路由机制根据共识强度、主诊医生判断或证据质量权重进行决策，而非简单计票。在四种LLM基线和MIMIC-IV数据集上的诊断预测与住院过程生成任务中，CAMP持续优于强基线，且比多数多代理方法更省计算资源。投票记录与仲裁轨迹提供透明化决策审计。
原文摘要 · Abstract (English)
Large language models applied to clinical prediction exhibit case-level heterogeneity: simple cases yield consistent outputs, while complex cases produce divergent predictions under minor prompt changes. Existing single-agent strategies sample from one role-conditioned distribution, and multi-agent frameworks use fixed roles with flat majority voting, discarding the diagnostic signal in disagreement. We propose CAMP (Case-Adaptive Multi-agent Panel), where an attending-physician agent dynamically assembles a specialist panel tailored to each case's diagnostic uncertainty. Each specialist evaluates candidates via three-valued voting (KEEP/REFUSE/NEUTRAL), enabling principled abstention outside one's expertise. A hybrid router directs each diagnosis through strong consensus, fallback to the attending physician's judgment, or evidence-based arbitration that weighs argument quality over vote counts. On diagnostic prediction and brief hospital course generation from MIMIC-IV across four LLM backbones, CAMP consistently outperforms strong baselines while consuming fewer tokens than most competing multi-agent methods, with voting records and arbitration traces offering transparent decision audits.
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。