  In this article, we perform a review of the state-of-the-art of hybrid
machine learning in medical imaging. We start with a short summary of the
general developments of the past in machine learning and how general and
specialized approaches have been in competition in the past decades. A
particular focus will be the theoretical and experimental evidence pro and
contra hybrid modelling. Next, we inspect several new developments regarding
hybrid machine learning with a particular focus on so-called known operator
learning and how hybrid approaches gain more and more momentum across
essentially all applications in medical imaging and medical image analysis. As
we will point out by numerous examples, hybrid models are taking over in image
reconstruction and analysis. Even domains such as physical simulation and
scanner and acquisition design are being addressed using machine learning grey
box modelling approaches. Towards the end of the article, we will investigate a
few future directions and point out relevant areas in which hybrid modelling,
meta learning, and other domains will likely be able to drive the
state-of-the-art ahead.
