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 argue that we should typically expect this failure to transport, and we present common sources for it, divided into those under the control of the experimenter and those 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.

人工智能在医疗保健领域的日益普及凸显了这样一个问题：在训练地点实现超人类临床表现的计算模型在新地点可能表现会差很多。从这个角度来看，我们认为我们通常应该预料到这种传输失败，并且我们提出了它的常见来源，分为实验者控制下的来源和临床数据生成过程固有的来源。在固有来源中，我们更深入地研究可能影响数据分布的特定地点的临床实践，并提出一种潜在的解决方案，旨在将这些实践对数据的影响与常见的疾病因果模式隔离开来。概率临床模型的目标。