Medical Deep Learning -- A systematic Meta-Review

Deep learning had a remarkable impact in different scientific disciplines during the last years. This was demonstrated in numerous tasks, where deep learning algorithms were able to outperform the cutting-edge methods, like in image processing and analysis. Moreover, deep learning delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even contexts where deep learning outperformed humans, like object recognition and gaming. Another field in which this development is showing a huge potential is 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 centres, like hospitals, but it relates also to data collected by general practitioners, mobile healthcare apps, or online websites, just to name a few. This trend resulted in new, massive research efforts during the last years. 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. Hence, a complete overview of the field of $'$medical deep learning$'$ is almost impossible to obtain and getting a full overview of medical sub-fields becomes increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been presented within the last years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as foundation, the aim of this contribution is to provide a very first high-level, systematic meta-review of medical deep learning surveys.