Medical Deep Learning - A systematic Meta-Review

Jan Egger, Christina Schwarz-Gsaxner, Antonio Pepe, Jianning Li

Research output: Working paperPreprint


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 state-of-art methods, also in image processing and analysis. Moreover, deep learning delivers good results in tasks like autonomous driving, which could not have been performed automatically before. There are even applications where deep learning outperformed humans, like object recognition or games. 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 an automatic and reliable processing and analysis of this information. Patient data is not only collected in clinical centres, like hospitals, but it relates also to data coming from general practitioners, healthcare smartphone 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 returns 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 gets increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been presented within the last years. They focused, in general, on specific medical scenarios, like the analysis of medical images containing specific …
Original languageEnglish
Publication statusPublished - 28 Oct 2020

Publication series e-Print archive
PublisherCornell University Library

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