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 probabilistic clinical models.

翻译：人工智能在医疗保健领域的日益普及凸显了一个问题：一个在训练机构达到超人类临床性能的计算模型，在应用于新机构时性能可能显著下降。本观点性文章阐述了导致这种迁移失效的常见原因，将其分为实验者可控制因素与临床数据生成过程固有因素两类。在固有因素中，我们重点探讨了可能影响数据分布的特定机构临床实践，并提出了一个潜在解决方案，旨在将这些实践对数据的影响与概率性临床模型通常关注的疾病因果模式分离开来。