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P3审计框架与Cancer JEPA医学世界模型论文
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事件围绕两项医学AI研究展开:一是Patient, Place, Prior (P$^3$) 审计框架,二是Cancer JEPA医学世界模型论文。2026-10-08 12:00,arXiv Machine Learning Theory(一手)发表P$^3$框架论文,提出用于审计医学世界模型个性化程度的框架,检验医学预测是否真正受益于患者纵向影像史与患者匹配的空间支撑,并在匹配条件下超越群体平均预测。目前公开信息仅涉及该框架的目标与检验维度,未披露具体实验数据、对比基线或Cancer JEPA论文的详细结果。
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Oct 8, 2026
- arXiv · Machine Learning TheoryPatient, Place, Prior (P$^3$):医学世界模型中什么才算个性化?
论文提出 Patient, Place, Prior (P$^3$) 审计框架,检验医学预测是否真正受益于患者纵向影像史、患者匹配的空间支撑,并在匹配条件下超越群体平均预测。
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