发表机构
Diastole Medical R&D（Diastole医疗研发机构）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对医疗世界模型同步预处理可能抹去任务相关证据的问题，提出EndoClock审计方法及四分类法，以超声心动图为例验证其有效性，为医疗AI流程审计提供可执行方案。
AI 中文摘要
医疗世界模型通常从同步到固定速率网格的多模态记录中学习，这种预处理会将每个原生数据流重采样到共享时间轴上。每个流都有一个控制观测值发射或更新的观测时钟，当时钟依赖于隐状态或采集状态时，它是内生的。在这种情况下，同步可能并非中性，会在模型看到数据前抹去与任务相关的证据。我们提出一种四分类法，用于表征区分目标事件或状态所需的证据保留位置：相关见证可能保留在采样值、网格单元更新模式、原生时序中，或仅保留在外部采集通道中。EndoClock将该分类法实现为保守的预训练审计，它报告可用证据支持的最低含见证表征，若无法确定分类则报告未解决。我们以超声心动图为例说明该失败：脉冲波多普勒采集期间B型视频输出停止，对应测量事件仅记录在外部采集日志中。本研究是一项初步的失败预警与可执行审计，其实际意义在于需保留原生观测过程足够久，以确定同步是否抹去了预期任务所需的信息。
英文摘要
Medical world models commonly learn from multimodal recordings synchronized onto a fixed-rate grid. This preprocessing resamples each native stream onto a shared time axis. Each stream has an observation clock that governs when observations are emitted or updated. When this clock depends on the latent or acquisition state, it is endogenous. In such settings, synchronization may not be neutral and can erase task-relevant evidence before the model sees the data. We introduce a four-regime taxonomy that characterizes where the evidence needed to distinguish a target event or state survives. The relevant witness may remain in the sampled values, in grid-cell update patterns, in native timing, or only in an external acquisition channel. EndoClock operationalizes this taxonomy as a conservative pretraining audit. It reports the lowest witness-bearing representation supported by the available evidence, or unresolved when no regime can be established. We illustrate this failure in echocardiography, where B-mode video write-outs cease during pulsed-wave Doppler acquisition while the corresponding measurement events remain recorded only in an external acquisition log. This work is a preliminary failure alert and executable audit. Its practical message is to preserve the native observation process long enough to determine whether synchronization has erased information required by the intended task.
CommentsAccepted for publication at the 1st MICCAI Workshop on Medical World Models (MWM 2026)
