The rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sites may perform substantially worse at new sites. In this perspective, we present common sources for this failure to transport, which we divide into sources under the control of the experimenter and sources inherent to the clinical data-generating process. Of the inherent sources we look a little deeper into site-specific clinical practices that can affect the data distribution, and propose a potential solution intended to isolate the imprint of those practices on the data from the patterns of disease cause and effect that are the usual target of clinical models.

翻译：人工智能在医疗领域的日益普及凸显了一个问题：一个在其训练站点上达到超人类临床性能的计算模型，在新站点上可能表现显著下降。本文从这一视角出发，梳理了导致这种迁移失败的常见原因，并将其分为实验者可控制的因素与临床数据生成过程中固有的因素两类。针对固有因素，我们进一步深入探讨了可能影响数据分布的特定站点临床实践，并提出了一种潜在解决方案，旨在将这些实践在数据中留下的印记与临床模型通常关注的疾病因果模式分离开来。