保真度陷阱与反馈陷阱：医疗数字孪生作为模块化演化因果系统的案例
The Fidelity and Feedback Traps: The Case for Health Digital Twins as Modular Evolving Causal Systems
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中文总结 AI 辅助
本文指出医疗数字孪生存在保真度与反馈陷阱，提出应将其视为因果有效、模块化且可演化的系统，其评估标准应为支持决策的效果而非观测现实的忠实度。
中文摘要 AI 辅助
医疗数字孪生可用于比较治疗方案、预测患者轨迹并支持临床决策。尽管与机械数字孪生相关，但最初为工程应用开发的数字孪生，若直接复制机械数字孪生的架构与目标，在医疗领域可能失效，原因有二：其一为保真度陷阱，即认为准确的模型仅凭精度就能回答假设性问题，而预测与反事实推理是不同任务，能很好拟合过往轨迹的孪生体在治疗方案排序时可能出错；其二为反馈陷阱，即孪生体基于自身推荐生成的数据进行更新，如此重新拟合可能恢复有偏关系，且即便数据愈发稀少，其置信度仍会提升。本文主张，医疗数字孪生应被视为因果有效、模块化且可演化的系统：模块化可隔离干预性推荐所需的数据与模型，因果有效性支撑此类主张，受管控的演化则在更新孪生体的同时，考虑其推荐如何重塑数据。最终结论是，医疗孪生体的标准应是其在自身助力构建的现实中支持决策的效果，而非其对所观测现实的忠实再现程度。
英文摘要
Digital twins for health may be used to compare treatments, project patient trajectories, and support clinical decisions. While related to mechanical digital twins, those initially developed for engineering applications, replicating the mechanical digital twin architecture and goals may fail in health for two reasons. The fidelity trap is the belief that an accurate model can answer what-if questions by virtue of its accuracy. Prediction and counterfactual reasoning are different tasks, and a twin that can fit past trajectories well may miss the mark when ranking treatments. The feedback trap arises when the twin updates on data its own recommendations helped generate. Refitting in this way can recover a biased relationship and grow more confident even as data grows thinner. We contend that health digital twins should be conceived as causally valid, modular, and evolving systems. Modularity isolates the data and models needed for interventional recommendations, causal validity supports such claims, and governed evolution updates the twin while accounting for how its recommendations reshape the data. We conclude that the standard for a health twin should be how well it supports decisions in the world it helps create, not how faithfully it reproduces the world it observes.
