  Artificial intelligence (AI) generally and machine learning (ML) specifically
demonstrate impressive practical success in many different application domains,
e.g. in autonomous driving, speech recognition, or recommender systems. Deep
learning approaches, trained on extremely large data sets or using
reinforcement learning methods have even exceeded human performance in visual
tasks, particularly on playing games such as Atari, or mastering the game of
Go. Even in the medical domain there are remarkable results. The central
problem of such models is that they are regarded as black-box models and even
if we understand the underlying mathematical principles, they lack an explicit
declarative knowledge representation, hence have difficulty in generating the
underlying explanatory structures. This calls for systems enabling to make
decisions transparent, understandable and explainable. A huge motivation for
our approach are rising legal and privacy aspects. The new European General
Data Protection Regulation entering into force on May 25th 2018, will make
black-box approaches difficult to use in business. This does not imply a ban on
automatic learning approaches or an obligation to explain everything all the
time, however, there must be a possibility to make the results re-traceable on
demand. In this paper we outline some of our research topics in the context of
the relatively new area of explainable-AI with a focus on the application in
medicine, which is a very special domain. This is due to the fact that medical
professionals are working mostly with distributed heterogeneous and complex
sources of data. In this paper we concentrate on three sources: images, *omics
data and text. We argue that research in explainable-AI would generally help to
facilitate the implementation of AI/ML in the medical domain, and specifically
help to facilitate transparency and trust.
