In the last decade, the deep learning community has made considerable progress in the modeling of complex data, in particular with image data. Artificial neural networks are a core building block that allows for flexible non-linear functions in the corresponding models. Furthermore, deep generative approaches can be used to model the joint distribution of variables, and to subsequently draw synthetic observations. Recent techniques also allow to combine neural networks with differential equations for dynamic modeling. Many of these approaches have a black-box character, i.e. do not meet the requirement of interpretability needed in typical application settings of applied biostatisticians. Yet, an “explainable artificial intelligence (AI)” movement is currently emerging that strives to fill this gap. I will use exemplary applications to illustrate how corresponding techniques can be used to perform complex modeling tasks while still obtaining interpretability. In a first example, I will show how synthetic observations from deep generative models can be useful for uncovering structure in single cell gene expression data. In a second example, I will demonstrate how a combination with differential equations allows to incorporate domain knowledge, and to characterize individual dynamic trajectories. Together, these examples will illustrate how applied biostatisticians can extend their toolbox with developments from the deep learning community, and also contribute their own perspective.