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English(EN) SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction

新型AI代理通过减少不必要的检查来优化癌症诊断

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了SAGEAgent，这是一种新颖的基于LLM的临床代理，旨在优化癌症患者的诊断模态获取。与先前假设数据完全可用或被动处理缺失信息的旧方法不同，SAGEAgent根据患者的具体需求和不断增加的临床负担，主动决定执行哪些诊断测试。这种方法旨在平衡预测准确性与诊断程序的侵入性，从而可能减少不必要的检查和相关成本。 AI

影响 这项研究通过减少侵入性和昂贵的检查次数，可能带来更高效、更具成本效益的癌症诊断。

排序理由 该集群包含一篇详细介绍新AI模型及其实验结果的研究论文。

在 arXiv cs.AI 阅读 →

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新型AI代理通过减少不必要的检查来优化癌症诊断

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Chongyu Qu, Can Cui, Zhengyi Lu, Junchao Zhu, Tianyuan Yao, Junlin Guo, Juming Xiong, Yanfan Zhu, Yuechen Yang, Bennett A. Landman, Yuankai Huo ·

    SAGEAgent：一种用于多模态生存预测中成本感知模态获取的自演化代理

    arXiv:2607.09521v1 Announce Type: new Abstract: 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 …

  2. arXiv cs.AI TIER_1 English(EN) · Yuankai Huo ·

    SAGEAgent：一种用于多模态生存预测中成本感知模态获取的自演化代理

    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 requiri…

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