这篇论文提出了SAGEAgent，它能像医生一样判断该给病人做哪些检查，在几乎不掉准确率的前提下把检查负担砍掉一半多，挺实用的。
SAGEAgent是一种基于LLM的临床智能体，能自主决定为每位癌症患者获取哪些诊断模态，平衡预测准确性与临床侵入性。它通过临床工具、情景记忆和语义记忆推理患者的诊断状态。在包含TCGA-LGG、TCGA-GBM和BraTS的胶质瘤队列中，使用四种诊断模态，SAGEAgent在保持竞争性生存预测准确率的同时，将平均获取负担降低了55%。
SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction
Does every cancer patient truly need a complete diagnostic workup for accurate survival prediction? In multimodal clinical oncology, diagnostic modalities follow a clinically mandated order of escalating burden -- from demographics collected at intake to genomic profiling requiring specialized tissue analysis. Current multimodal survival methods either assume all modalities are available or passively handle missing data, but none actively reason about whether acquiring the next modality is justified for a given patient along this ordered workflow. We formulate this as a sequential decision problem and propose SAGEAgent (Sequential Acquisition Guided by Experience), a self-evolving LLM-based clinical agent that decides which diagnostic modalities to acquire for each patient, balancing predictive accuracy against clinical invasiveness. SAGEAgent reasons about each patient's evolving diagnostic state through clinical tools that translate numerical predictions into text, an episodic memory that retrieves similar past cases, and a semantic memory that accumulates reusable decision patterns from experience. Experiments on a glioma cohort combining TCGA-LGG, TCGA-GBM, and BraTS with four diagnostic modalities demonstrate that SAGEAgent achieves competitive survival prediction accuracy while reducing average acquisition burden by 55%.