TumorBoard：基于证据的多智能体纵向神经肿瘤学决策支持系统
TumorBoard: Evidence-Grounded Multi-Agent Decision Support for Longitudinal Neuro-Oncology
浏览论文内容
中文总结 AI 辅助
TumorBoard是基于证据的多智能体纵向神经肿瘤学决策支持系统，经360例基准测试，其性能优于现有基线，安全管控模块可有效降低有害建议比例。
中文摘要 AI 辅助
神经肿瘤学决策需要对连续MRI、病理、分子标志物、治疗史、体能状态及更新指南进行协同解读。我们提出TumorBoard，这是一个围绕共享纵向病例状态与可审计主张-证据账本构建的多智能体决策支持系统。放射学、神经病理学、分子诊断、指南及治疗规划领域的专家智能体生成带来源的原子主张；对抗性评论者暴露矛盾；安全管控者依据证据充分性与时间有效性发布、限定或弃权（不执行）建议。在360例隐藏基准测试中，匹配 token 预算下，TumorBoard的动作F1达0.772，证据蕴含度达0.914；其性能超出最强的类型化委员会基线3.1个百分点（95%置信区间：1.6至4.7，校正后p值=0.0012），建议-证据覆盖率达0.927。在证据缺失场景下，系统对84.2%的不安全病例弃权（不执行），有害建议仅占5.8%；安全管控者使有害发布减少7.8个百分点，假弃权成本为4.3个百分点。对账本、评论者及管控者的消融研究产生了预期的失效模式，证实结构化协同是多智能体优势的来源。
英文摘要
Neuro-oncology decisions require coordinated interpretation of serial MRI, pathology, molecular markers, treatment history, performance status, and evolving guidelines. We present TumorBoard, a multi-agent decision-support system built around a shared longitudinal case state and an auditable claim-evidence ledger. Specialist agents for radiology, neuropathology, molecular diagnosis, guidelines, and therapy planning produce atomic claims with provenance. An adversarial critic exposes contradictions, and a safety governor releases, qualifies, or defers recommendations according to evidence sufficiency and temporal validity. On a 360-case hidden benchmark at a matched token budget, TumorBoard achieved an action F1 of 0.772 and evidence entailment of 0.914. It exceeded the strongest typed-council baseline by 3.1 percentage points (95% CI: 1.6 to 4.7, adjusted p = 0.0012), while recommendation-to-evidence coverage reached 0.927. Under evidence deletion, the system deferred 84.2% of unsafe cases and limited harmful recommendations to 5.8%. The safety governor reduced harmful release by 7.8 percentage points at a false-deferral cost of 4.3 percentage points. Ablation studies of the ledger, critic, and governor produced the predicted failure patterns, establishing structured coordination as the source of the measured multi-agent advantage.
