  Deep learning (DL) has remarkably impacted several different scientific
disciplines over the last few years. E.g., in image processing and analysis, DL
algorithms were able to outperform other cutting-edge methods. Additionally, DL
has delivered state-of-the-art results in tasks like autonomous driving,
outclassing previous attempts. There are even instances where DL outperformed
humans, for example with object recognition and gaming. DL is also showing vast
potential in the medical domain. With the collection of large quantities of
patient records and data, and a trend towards personalized treatments, there is
a great need for automated and reliable processing and analysis of health
information. Patient data is not only collected in clinical centers, like
hospitals and private practices, but also by mobile healthcare apps or online
websites. The abundance of collected patient data and the recent growth in the
DL field has resulted in a large increase in research efforts. In Q2/2020, the
search engine PubMed returned already over 11,000 results for the search term
'deep learning', and around 90% of these publications are from the last three
years. However, even though PubMed represents the largest search engine in the
medical field, it does not cover all medical-related publications. Hence, a
complete overview of the field of 'medical deep learning' is almost impossible
to obtain and acquiring a full overview of medical sub-fields is becoming
increasingly more difficult. Nevertheless, several review and survey articles
about medical DL have been published within the last few years. They focus, in
general, on specific medical scenarios, like the analysis of medical images
containing specific pathologies. With these surveys as a foundation, the aim of
this article is to provide the first high-level, systematic meta-review of
medical DL surveys.
