Deep learning [1] had a remarkable impact in different scientific fields during the last years. This was demonstrated in numerous tasks, where deep learning approaches were able to outperform the state-of-art methods, including image processing and analysis [2] , [3] . Moreover, deep learning delivers reasonable results in tasks that could not have been performed automatically before, like autonomous driving [4] , [5] . There are even applications where deep learning outperformed humans, like in object recognition [6] or games [7] , [8] .
A field in which this development started to show 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 [9] . Patient data is not only collected in clinical centers, like hospitals, but it relates also to data coming from general practitioners, healthcare smartphone apps, and online websites, just to name a few. This all resulted in new, massive research efforts during the last years. In Q2 of 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 previous three years.
However, even though PubMed represents the largest search engine in the medical domain, it does not cover all medical-related publications. For example, many primary venues for computer science research are renowned conferences [10] , [11] . Despite their high impact and regard within the community, conference proceedings are usually not listed under PubMed, with only a few exceptions, like the prestigious annual conference 'Medical Image Computing and Computer Assisted Intervention' (MICCAI). In addition, there are rather technical, non-interdisciplinary conferences, e.g. in computer vision, through which very influential research on medical applications is published [12] . These articles are usually overseen by the medical search engines.
This does not relate to survey and review articles, which, due to their length, are most generally published in peer-reviewed, PubMed-indexed journals. However, for this reason, it is possible for a review article to miss some relevant articles.
Taking all these considerations into account, a complete overview of the field of medical deep learning is almost impossible to obtain and getting full overviews of medical sub-fields becomes increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last years. They focused, in general, on specific medical scenarios, like the analysis of certain medical images containing specific pathologies, like the automatic detection of a cardiovascular disorder in computed tomography angiography [13] . Starting from these surveys, the aim of this contribution is to provide a very first high-level and systematic meta-review of medical deep learning surveys, equivalently to existing meta-reviews in the medical domain, like the systematic review of systematic reviews of homeopathy [14] , or the survey of surveys on the use of visualization for interpreting machine learning models [15] in a technical domain, and the authors are not aware of any meta-review in medical deep learning or even deep learning so far. Compared to medicine, which has a millennial tradition, computer science is a very young discipline.
Nonetheless, if this discipline continues growing with such pace, meta-reviews like this will become more and more common.
In this contribution, we present all review and survey articles published from 2017 to 2019, which were found after a systematic PubMed search (see Search Strategy). We did not include articles published after 2019, since the citations of the reviewed publications are here also presented. Thus, the relatively new reviews from 2020 are still 'under-cited' in comparison to the reviews from the previous years (as can also be seen in the decreasing number of citations for 2017: 6089 citations, 2018: 947 citations and 2019: 408 citations), and one aim of this contribution is to give an overall impression of the impact these works already had on their scientific fields. Table   1 gives an overview of the number of reviews published each year, from 2017 to 2019. Furthermore, the table shows the sum of the overall references and citations for each year according to Google Scholar (status as of August 2020). Tables 2-4 describe the publications of each year in more detail.
For this systematic meta-review, a search in PubMed for the keyword 'Deep Learning' together with any keyword between {'Review', 'Survey'} was performed. Based on the titles and abstracts, all records which were no actual review or survey contributions in the medical field, like [16] , [17] and [18] , or have not been written in English, like [19] , [20] , or are veterinarian reviews [21] , or are about a human learning strategy called Deep Learning [22] , were excluded (while the term "deep learning" was coined by Geoffrey Hinton in terms of learning deep neural networks in 2006 [23] , [24] , the term seemed to have existed much earlier in educational psychology [25] , which can be considered a nice coincidence). This ultimately resulted in a total number of 43 review or survey publications about deep learning in the medical field, which are covered within this systematic meta-review. Summarized, this high-level systematic meta-review provides an overview of the published medical deep learning reviews and surveys in PubMed, as well as their references and citations (status as of August 2020).
The main body of this contribution presents exclusively review and surveys on medical deep learning from a systematic PubMed search. To keep the manuscript concise for the reader, we provide only high-level summaries and excerpts, mainly form the review and survey abstracts (note that some of the presented reviews cover up to several hundred publications themselves). Thus, every review or survey publication will be summarized in around 100 to 200 words. However, by pointing to the associated publications via the keyword classifications and chronological arrangement of the presented medical deep learning reviews or surveys, the interested reader should be able to dive deeper into the specific categories and subcategories. The rest of this manuscript is organized as follows: Section two presents the overview of the medical deep learning reviews and surveys divided into the years of publications from 2017 to 2019, and a chronological order beginning with the first published work in the respective year. The final Section 3 concludes this contribution with a discussion and outlines areas of future directions.
This section presents an overview of review and survey publications in medical deep learning. The publications are arranged in three sub-sections by their year of publication, from 2017 to 2019. Within the yearly sub-sections, the publications are arranged chronologically by their date of publication starting with the first published work in the corresponding year. Typically, review or survey contributions order the reviewed publications in categories, like medical image classification, object detection, segmentation, registration, and other tasks. However, for this meta-review we decided explicitly for an order by publication date to show the historical sequence in which they occurred to the reader. Anyway, the tables provide also a quick overview of the different categories. Hence, at the beginning of every section (2017, 2018 and 2019), the areas of the reviews are summarized in a listing, which corresponds to the chronical order of the publications of this year in the following descriptions. Tables 1-4 are also   divided into the years 2017 to 2019 and chronically ordered. Moreover, the tables   present the number of referenced works and the current citations for every year and   publication according to Google Scholar, which unfolds an overall number of 5023 referenced works in the proposed reviews, and an overall number of 7444 citations for the reviews themselves (status as of August 2020).
With the proposed search strategy, there have been seven surveys identified in medical deep learning in 2017. Themselves, the proposed reviews from 2017 cite 1060 contributions and have been cited 6089 times (status as of August 2020). They cover the following categories (see also Table 2 Medical image analysis -The goal of medical image analysis is to automatically or semi-automatically extract information from patient data. In example, this could be an automatic determination of the tumour volume from a patient's magnetic resonance imaging (MRI) scan with the aim to choose the appropriate therapy strategy. Shen et al. [26] introduce in their publication the fundamentals of deep learning methods and review their success in fields like image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis. They conclude their work by raising research issues and suggesting future directions for further improvements in medical image analysis.
Healthcare -The umbrella term healthcare envelopes the maintenance or improvement of people's health via the prevention, diagnosis, treatment, recovery, or cure of disease, illness, injury, and other physical and mental impairments. In that context, the survey article of Miotto et al. [27] , reviews the recent literature on applying deep learning technologies to advance the healthcare domain. Based on the analysed publications, they conclude and suggest that deep learning-based approaches could be the vehicle for translating big biomedical data into improved human health. Furthermore, they depict limitations and the need for improved methods, development and applications, and discuss future challenges in this area.
Medical image analysis -The publication of Litjens et al. [28] reviews the major deep learning concepts pertinent to medical image analysis. They summarize over 300 contributions within the field and survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Further, concise overviews of studies are presented and categorized by application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, and musculoskeletal. Stroke management -Stroke can cause a long-term disability and a significant amount of attention has been paid to the use of neuroimaging to assess the potential benefit for identifying areas of ischemia that have not yet experienced cellular death. In this context, Feng et al. [29] review clinical applications for deep learning-guided stroke management. They identify the following key areas where deep learning may be imminently applied to stroke management: image segmentation, automated featurization (radiomics), and multimodal prognostication. Health-record analysis -Health-record analysis explores the digital information stored in electronic health databases. Originally intended for archiving patient information and performing administrative healthcare tasks like billing, many researchers have found secondary use of these records for various clinical informatics applications. Hence, Shickel et al. [31] survey in their review the research on applying deep learning to clinical tasks based on health-record analysis data. They found a variety of deep learning techniques and frameworks being applied to several types of clinical applications such as information extraction, representation learning, outcome prediction, phenotyping, and de-identification. They identify several limitations of current research within their review, including topics such as model interpretability, data heterogeneity, and lack of universal benchmarks. They conclude their review by summarizing the state-of-the-field and identifying avenues of future deep health-record analysis research.
Microscopy image analysis -Microscopy images are images produced by a microscope that can be used to characterize various diseases, like breast cancer, lung cancer or brain tumours. Xing et al. [32] provide a snapshot of the field for medical microscopy image analysis. They start with a brief introduction of popular deep neural networks and summarize current deep learning achievements in various tasks, including detection, segmentation, and classification in microscopy image analysis. Moreover, they explain the architectures and the principles of convolutional neural networks, fully convolutional networks, recurrent neural networks, stacked autoencoders, and deep belief networks, and interpret their formulations or modelling for specific tasks on various microscopy images. They conclude by discussing open challenges and the potential trends of future research in microscopy image analysis using deep learning.
With the proposed search strategy, 15 surveys have been identified in the area of medical deep learning in 2018. The proposed reviews from 2018 themselves refer to 1684 contributions and have been cited 947 times (status as of August 2020), and cover the following categories (Table 3) Toxicity of chemicals -Toxicological testing and evaluation of chemicals is important for humans and wildlife, who are by default exposed to natural and synthetic chemicals during their lifetime. Tang et al. [8] present a survey on how deep learning can be a useful tool for building quantitative structure-activity relationship models for toxicity prediction using large datasets, which traditional data analysis methods fail to effectively process. First, a brief technical background on deep neuronal networks is outlined. Then, the current state of chemical toxicity prediction models built with deep neuronal networks is reviewed. Finally, the relevant toxicity data sources are presented, possible limitations are discussed, and perspectives on deep neuronal networks utilization in chemical toxicity prediction are given.
Pulmonary nodules diagnosing -A pulmonary nodule is a small roundish shaped abnormal area or mass in the lung. In their review, Yang et al. [9] present deep learning works that aid the decision support for pulmonary nodules diagnosing. The deep learning methods they survey focus on computer-assisted feature extraction, nodule detection, false-positive reduction, and benign-malignant classification for the huge volume of chest scan data.
Physiological signals -Physiological signals are signals form psycho-physiological measures. In their survey, Faust et al. [10] review deep learning approaches for healthcare applications based on such physiological signals. Their bibliometric analysis shows that the reviewed papers focused mainly on Electromyograms (EMGs),
(EOGs). Hence, they used these four categories to structure the content of their survey.
DNA sequencing -Deoxyribonucleic acid (DNA) sequencing is the process of determining the order of nucleotides in DNA by sequencing the nucleic acid. In that respect, Celesti et al. [11] review deep learning as a possible approach to speed up the DNA sequencing process, especially in regards to the fact that large amount of genomics data, originated by emerging next-generation sequencing (NGS) techniques, requires more and faster algorithms. They present a taxonomic analysis highlighting the major deep learning software solutions for NGS and discuss future challenges in the perspective of cloud computing services.
Radiotherapy -Radiotherapy is used, in example, in the treatment of cancer patients. However, the planning and delivery of radiotherapy treatment is a complex process, which artificial intelligence tries to automate and therefore facilitate. In their review, Meyer et al. [37] start explaining the concept of deep learning and addressing it in the broader context of machine learning. They introduce the most common network architectures, with a more specific focus on convolutional neural networks.
Subsequently, they present an overview of the contributions on deep learning methods that can be applied to radiotherapy. They classify them into seven categories related to the patient workflow.
Ophthalmology -The diagnosis and treatment of eye disorders in medicine is called ophthalmology. Grewal et al. [38] survey deep learning as an emerging technology with numerous potential applications in ophthalmology. They review deep learning tools that have been applied to different diagnostic modalities, like digital photographs, optical coherence tomography and visual fields, and in the assessment of various disease processes including cataracts, glaucoma, age-related macular degeneration, and diabetic retinopathy. Omics -Omics, such as genomics, transcriptome-and proteomics, has also been heavily affected by the era of big data. Zhang et al. [43] aim to provide an entry-level review for researchers, to understand and use deep learning-based methods in order to solve omics problems. They outline several deep learning models and discuss various research areas that have combined omics and deep learning. Furthermore, they compare the features and performance of open source deep learning-based frameworks, and propose future opportunities and challenges.
Sport-specific movement recognition -Sport-specific movement recognition can be used for an objective assessment of an athlete's performance, e.g. in elite sports.
Cust et al. [44] review the automated detection and recognition of sport-specific movements, which can overcome the limitations associated with manual performance analysis methods. They systematically review the literature on machine and deep learning for sport-specific movement recognition using inertial measurement unit or computer vision data inputs. They conclude that the adaptation of the experimental set-up, the data pre-processing, and the model development methods are best considered in relation to the characteristics of the targeted sports movement or movements.
Diabetic retinopathy -Diabetes is rising worldwide and diabetic retinopathy is the most common microvascular complication, which can lead to visual impairment or even blindness. Nielsen et al. [45] performed a systematic review of deep learningbased algorithms for the screening of diabetic retinopathy. They aimed to identify studies that incorporated the use of deep learning in classifying full-scale diabetic retinopathy in retinal fundus images of patients with diabetes under the conditions that the studies provide a diabetic retinopathy grading scale, a human grader as a reference standard and a deep learning performance score.
Image cytometry -Cytometry is the measurement of cell characteristics, like the cell size, the cell count, the cell morphology, the cell cycle phase and the DNA content. Gupta et al. [46] review how deep learning has been applied to microscopy image data of cells and tissue samples. They begin with an overview of neural networks and deep learning. While outlining considerations regarding input data requirements, computational resources, challenges, and limitations, they review published works on deep learning in image cytometry and identify methods that have not yet been applied to cytometry data for future work.
Radiology -Radiology is the medical field of extracting useful information from images, like computed tomography (CT) or MRI patient acquisitions, for diagnosis and treatment of humans and animals. In their review, Mazurowski et al. [47] discuss the general context of radiology and opportunities for application of deep learning-based methods. They also introduce basic concepts of deep learning, such as convolutional neural networks. Next, they present the research contributions in deep learning applied to the radiology discipline, organizing the reviewed studies by the specific tasks types that they attempt to solve. They conclude their work by discussing opportunities and challenges for incorporating deep learning-based approaches into the radiology practice.
With the proposed search strategy, 21 surveys have been identified in the area of medical deep learning in 2019. The proposed reviews from 2019 refer to 2279 contributions and have been cited already 408 times (status as of August 2020), and cover the following categories (Table 4) Medical imaging -Medical imaging covers the field of producing visual representations of the interior of a body, in example using computed tomography, magnetic resonance imaging or ultrasound, just to name a few. Biswas et al. [48] provide a survey of various types of deep learning systems available with a focus on current applications of deep learning in medical imaging. They also outline the transition of technology from machine learning to deep learning and provide an analysis of complexities involved and possible benefits accrued by the users and developers.
Brain cancer classification -In general, brain tumors are classified into several types, depending on whether they are, for example, benign or malignant, which helps to choose an optimal treatment for the patient. Tandel et al. [49] reviewed the pathophysiology of brain cancer, modalities for brain cancer imaging and automatic computer assisted methods for brain cancer characterization in machine and deep learning. Moreover, they outlined the relationship between brain cancer and other brain disorders in the context of deep learning approaches, like stroke, Alzheimer's, Parkinson's, and Wilson's disease, leukoriaosis, and other neurological disorders.
Electroencephalogram -In neuroscience, EEG analysis is an important technique with applications in neuroscience and neural engineering, like braincomputer interfaces (BCIs). Craik et al. [50] performed a systematic review on deep learning applications for EEG classification, addressing several questions, like which EEG classification tasks have been explored with deep learning. They analyzed the studies based on several categories, like EEG preprocessing methods, input type or the deep learning architecture, and the deep learning task were divided into five groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each task type, they describe the specific input formulation, major characteristics, and end classifier recommendations.
Pulmonary nodule detection -As mentioned before for the pulmonary nodules diagnosing review, a pulmonary nodule is a small roundish shaped abnormal area or mass in the lung. Pehrson et al. [51] propose a systematic review of automatic pulmonary nodule detection using deep or machine learning algorithms applied to a common dataset, the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. They divided the works into two subcategories based on their overall architecture. The conclude that machine learning and deep learning algorithms are able to detect lung nodules with a high level of accuracy, sensitivity, and specificity, however, also conclude that there is no consensus on a method to determine the efficiency of machine learning algorithms.
Neuro oncology -Gliomas is a type of cancer that represent 80% of all primary malignant brain tumors. Shaver et al. [52] survey seeks to summarize the current deep learning applications used in the field of glioma detection and outcome prediction. They focus on pre-and post-operative tumour segmentation, genetic characterization of tissue, and prognostication. They show and conclude that deep learning approaches for segmenting, characterizing, grading, and predicting the survival in gliomas are promising opportunities.
Diabetic retinopathy -Another survey about diabetic retinopathy was published by Asiri et al. [53] . They focus on deep learning-based computer-aided diagnosis (CAD) systems, which they structure into various stages such as detection, segmentation and classification of lesions in fundus images. Furthermore, they discuss pros and cons of published deep learning methods to accomplish these tasks.
Cardiac arrhythmia -An ECG is the most common technique for detecting cardiac arrhythmia, because of its simplicity and low cost, but the massive amount of ECG data collected every day, in home and hospital, precludes a manual data review.
Parvaneh et al. [54] performed a review of recent advancements on cardiac arrhythmia detection using deep learning approaches. They summarized existing literature from five aspects, namely utilized dataset, application, type of input data, model architecture, and performance evaluation, and present limitations of the reviewed works and potential future opportunities.
Protein structure -The three-dimensional form of local segments of proteins is called protein secondary structure. Wardah et al. [55] propose a review on predicting the protein secondary structure using neural networks and deep learning methods. They start with an informative introduction to protein secondary structure and artificial neural networks, and conclude that there is much room for further improvement in the prediction of secondary structures in silico.
Electroencephalography -As stated beforehand in the review about EEG classification, EEG analysis is an important yet difficult task, with applications in neuroscience and neural engineering, which requires several years of training, because of its complexity. Roy et al. [56] performed a systematic review on deep learning-based electroencephalography analysis, covering various applications domains, like epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. In addition, they collected information pertaining to the data, the pre-processing methodology, the deep learning design choices, the results, and the reproducibility of the experiments from their reviewed studies.
Neurology -The medical branch related to disorders of the nervous system is Radiation oncology -A physician or doctor who is specialized in the treatment of cancer using ionizing radiation, like radionuclides or megavoltage X-rays, is called a radiation oncologist. In that context, Boldrini et al. [60] performed a literature review in PubMed/Medline with a search strategy including the search terms "radiotherapy" and "deep learning". They identify recent publications on deep learning in radiation oncology, which they presented with a focus on clinically oriented readers. The review shows how deep learning can aid clinicians in their daily work, such as by reducing segmentation times, or predicting treatment outcomes and toxicities. However, they conclude that these techniques have not yet been employed in the clinical routine, and it remains to be seen how well they translate into practice.
Drug-drug interaction -Drug-drug interactions (DDIs) can cause adverse drug effects that have the potential to threaten public health and patient safety, hence, these interactions are crucial for drug research and pharmacovigilance. Zhang et al. [61] reviewed recent deep learning approaches that have been applied to the extraction of DDIs. They briefly describe each method and systematically compare its performance within the DDI corpus, and summarize the advantages and disadvantages. Finally, they conclude and discuss several challenges and future perspectives of DDI extraction for deep learning approaches.
Urology -The medical branch of urology is focused on surgical and medical diseases of the (fe)male urinary-tract system and the male reproductive organs, which include the kidneys, adrenal glands, ureters, urinary bladder, urethra, and the male reproductive organs (testes, epididymis, vas deferens, seminal vesicles, prostate, and penis). Suarez-Ibarrola et al. [62] review current and future applications of machine and deep learning in urology, with a focus on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. This covers, for example, the prediction of endourologic surgical outcomes in urolithiasis, the differentiation between benign and malignant small renal masses, the employment of radiomics and texture feature analysis for the distinction between low-and high-grade tumours in bladder cancer, and algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction for prostate cancer. Ophthalmic diagnosis -Sengupta et al. [64] another review focused on ophthalmology, focusing on ophthalmic diagnosis using deep learning approaches based on fundus images (rear of an eye). They describe various retinal image datasets that can be used for deep learning tasks, including segmentation of the optic disk, the optic cup, blood vessels as well as lesion detection. Furthermore, they discuss recent deep learning approaches for disease classification, like age-related macular degeneration, glaucoma, and diabetic retinopathy.
Alzheimer -Alzheimer or Alzheimer's Disease (AD), one of the leading causes of death in developed countries, is provoked by a chronic neurodegenerative disease that usually starts slowly and gradually worsens over time. In this regard, Ebrahimighahnavieh et al. [65] propose a systematic state-of-the-art review in deep learning for the automatic detection of Alzheimer's disease from neuroimaging. They focus on the extraction of useful biomarkers and features, like personal information, genetic data, and brain scans, the necessary pre-processing steps, and different ways of dealing with neuroimaging data originating from single-modality and multi-modality studies. Moreover, they present the performance of the deep learning models in AD detection, but also conclude that there are several limitations, especially regarding the availability of datasets and training procedures.
Pulmonary nodule detection -Another systematic review of deep learning in pulmonary nodule detection was published by Li et al. [66] . They focus on nodule detection and classification on various datasets that have not been derived from the LIDC-IDRI database. They found that three types of deep learning architectures are commonly used, namely convolutional neural networks, massive training artificial neural network, and deep stacked denoising autoencoder extreme learning machine methods. They conclude that deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.
Liver masses -A liver mass is a lump in the liver that can be caused by an abnormal cell growth, a cyst, hormonal changes, or an immune reaction, but is not necessarily cancer. Azer [67] performed a systematic review on deep learning approaches with convolutional neural networks (CNNs) for the identification of liver masses and hepatocellular carcinomas (HCCs). PubMed, EMBASE, the Web of Science, and research books were systematically searched, identifying works analyzing pathological anatomy, cellular, and radiological images on HCC or liver masses.
Further, the accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were presented with a primary focus on analyzing the type of cancer or liver mass, and identifying the type of images that showed an optimum accuracy in cancer detection.
Pulmonary medical imaging -Ma et al. [68] present a survey on deep learning for pulmonary medical imaging, where topics include classification, detection, and segmentation tasks with respect to pulmonary medical images, datasets, and benchmarks. They give an overview of the reviewed methods implemented on various lung diseases consisting of pulmonary nodules, pulmonary embolisms, pneumonia, and interstitial lung disease. Finally, they discuss the application of deep learning techniques, future challenges and potential directions in the area of medical imaging.
In this work, reviews and surveys on medical deep learning have been presented in a systematic meta-review contribution. A systematic search has been performed in the common medical search engine PubMed, which resulted in over 40 review or survey publications published during the last three years. In addition to a brief summary of each survey, the references and citations of these reviews are presented (status as of August 2020).
Before 2017, no medical deep learning review article has been indexed under PubMed according to the proposed search strategy. This makes sense, because even though these kind of approaches have already been suggested and applied at the end of the last century [69] , [70] , deep learning-based approaches only started to gain massive popularity after the convolutional neural network architecture AlexNet [71] won the ImageNet challenge in 2012. From that moment on, deep learning and convolutional neural networks have received an inexorable increasing attention in various communities, including medical image analysis, however, it took some time to have enough published works for the first review or survey articles. In addition, there is also a massive number of review and survey articles in other disciplines. To give a rough impression of these, we performed an additional non-systematic search, which is, however, far from complete and the results are only presented in a systematic listing, because these works would go far beyond the scope of this contribution. Nonetheless, they may be an inspiration for interested readers and we arranged them in three categories:
Object detection [72] , [73] , [74] Image segmentation [75] , [76] Face recognition [77] , [78] , [79] Action/motion recognition [80] , [81] Biometric recognition [82] , [83] Image super-resolution [84] Image captioning [85] Data augmentation [86] Generative adversarial networks [87] 2. Language processing General language processing [88] Language generation and conversation [89] , [90] , [91] , [92] Named entity recognition [93] , [94] Sentiment analysis [95] , [96] Text summarization [97] Answer selection [98] Word embedding [99] , [100] Financial forecasting [101] 3. Further works Big data [102] , [103] , [104] Reinforcement learning [105] , [106] , [107] Mobile and wireless networking [108] Mobile multimedia [109] Multimodal learning [110] Remote sensing [111] Graphs [112] Anomaly detection [113] Recommender systems [114] Agriculture [115] Multiple areas [116] Discussion -Typically, new trends in image processing are applied at first to general computer vision tasks, e.g. to 2D photos, before they are adapted and translated to tasks in the medical domain. This has several reasons. Firstly, 2D image processing is much less computationally intensive compared to processing large 3D image volumes from CT or MRI acquisitions. Secondly, the algorithms have to be more complex for 3D volumes than for 2D image processing, because they need to process one more dimension (or even more dimensions, if several scans at different time points have been acquired). Thirdly, often several image modalities and volumes, like combined positron emission tomography-computed tomography (PET-CT) scans, are available, and processing them jointly leads to information gain, but also increases the complexity. This is even more cumbersome if scans from different time points and/or different modalities, like CT and MRI, are not registered to each other. Finally, yet importantly, medical data is much harder to acquire and collect, especially in large quantities, not only because of the very time consuming, often slice-by-slice manual ground truth generation and memory capacities, but also because of privacy concerns. Medical data is usually highly sensitive and personal, and therefore, using it for research purposes requires institutional review board (IRB) approvals and patient consent. Generally, data has to be anonymized, e.g. by the removal of so-called patient tags (from the original DICOM scans) and other meta-information from the images and corresponding files, including name, sex and birth date. However, this is relatively easy compared to patient information that is encoded within the images themselves, like the patient's face in a head scan. Removing the eye area for patient de-identification within a 3D volume is possible, but laborious, because it has to be done manually for every scan to make sure the volumes are properly de-identified. A fully automatic approach is conceivable, yet highly risky and potentially disastrous if it fails for one single case. For head scans, deidentification by removing the eye area can be an option if the research is performed on a structure in another area of the head, like the lower jawbone [119] , [120] , but on the downside, it can render the images unusable for applications requiring the entire volume, in example, facial-based medical augmented reality for the head and neck area, which needs all facial features to work [121] , [122] . The IRB may allow the usage of the medical data for research purposes, but only within their own institution. This means that researchers from other institutions cannot re-use the exiting data to validate published results or build upon existing methods to push the research boundaries. As a result, a considerable amount of effort has to be invested into obtaining IRB approval, acquiring data and anonymization, which may delay new research by a few months, at best. Therefore, for large collections or rare pathological cases, it can easily take several years to establish a new database. Nonetheless, and against all odds, the massive amount of medical deep learning contributions is still increasing, and the proposed search strategy already reveals over 50 reviews or surveys for deep learning in PubMed by August 2020, which is more than all reviews from 2017-2019 together, which have been outlined within this contribution. TensorFlow [123] , PyTorch [124] or Caffe [125] , just to name a few. These frameworks do not necessarily require an in-depth education in computer science. In contrast, in the era before deep learning, very good coding skills in programming languages like C or C++ were required to implement complex image processing algorithms. Factors like optimization for a reasonable runtime played a much larger role, as hardware was much weaker a few years ago.
- (2) Related to the first reason, most deep learning libraries and toolkits use Python (https://www.python.org/), which is a high-level, interpreted programming language, and therefore, easier to learn, apply and deploy compared to the aforementioned, compiled programming languages, like C or C++. -(4) Another reason for the massive corona-like spreading of deep learning (note that there is also already a review about artificial intelligence / deep learning techniques in imaging data acquisition, segmentation and diagnosis for covid-19 [126] , and another one is on the horizon [127] ), is that many researchers make their code publicly available to the research community, which is easily possible thanks to common online repositories, like GitHub (https://github.com/) or GitLab (https://about.gitlab.com/). Because most implementations use common deep learning toolkits, they can often be applied to new data without too much adaption.
- (5) Photos, which identified two black persons as gorillas back in 2015 [129] . Someone could argue that a human raised and educated in a sexist or racist environment might also develop a similar behavioral attitude: Whether the algorithm did this willingly is more of a philosophical discussion. Interestingly, Google "fixed" the problem by removing and blocking the image categories "gorilla", "chimp", "chimpanzee" and "monkey". So, in summary, even leading tech companies face difficulties when it comes to ensuring that the output of data-driven algorithms does not lead to prejudices, racism [130] , and further publications showed how deep neural networks can easily be fooled, like [131] .
formulate it provocatively, some tasks like medical image segmentation have already been solved over thirty years ago, as can be seen by the claims within the countless publications released in the past years. In addition, the entire computer vision field seems to move from a general hot topic to another one over the years, like deformable models in the late '80s [132] , graph-based approaches in early '00s [133] , and, finally, deep neural networks after 2010 [134] , to name a few. This is also reflected by the sharp drop or rise of citations for these publications, depending on the addressed methodology. A more realistic picture of the feasibilities of the proposed works during these times may be biomedical challenges, where authors are encouraged to develop algorithms for a specific task [135] , in example the very influential brain tumor segmentation (BraTS) challenge, about the automatic segmentation of brain tumors or our new AutoImplant challenge from 2020 [136] , about automatic cranial implant design. The quantitative and qualitative evaluation results are often presented afterwards in a compact summary publication [137] . This definitely enables are more objective view on what is currently possible with the state-of-the-art, even though such challenges usually cannot replace a real evaluation in a clinical setting.
Finally, it should be mentioned that most medical deep learning applications are still in an early phase of development and have not yet found their way into the real clinical practice. This stands in strong contrast to non-learning approaches, like those used in medical navigation systems for neurosurgery [138] , [139] . Nevertheless, some deep learning experts claim that just adding enough (training) data will automatically lead to perfect results. Contradictory to this opinion, cars have driven already millions of kilometers to acquire training data, but fully selfdriving capabilities are still far away from being reliable, especially under different weather and light conditions. In this light, Tesla recently removed the "full self-driving"
option from its car store on its website. It should also be noted that there are domains were such a massive amount of data will never be available, in example in the medical field. Certain pathologies are simply (and thankfully) not frequent enough, so even by collecting all the patient data for this pathology from the hospitals around the world, and applying additional data augmentation methods [140] , there still might not be enough for training powerful algorithms.
Nonetheless, there have been certain tasks where machine learning has undoubtedly outperformed humans already. Examples are Deep Blue [141] and
AlphaGo [142] in games, where deep learning algorithms could even beat the best (known) human players around the world. However, these tasks have strong constraints by fixes rules, like in chess or Go, on which algorithms can rely on. In contrast, medical tasks usually do not follow such rules and theoretically, unlimited possibilities exist. In example, a brain tumor [143] looks different for every patient in terms of shape, size, texture, etc. Another example is the human voice, with individual pitches and pronunciations, and further the inter-human variations when expressing different emotions [144] . In addition, algorithms can fall back on a massive database of pretrained games and game moves, without any further uncertainty. Another example where deep learning works very well in practice is the automatic detection and analyzation of car licenses. Despite several challenges and uncertainties, like different fonts, colors, languages, complex backgrounds, deformities, hazardous situations, occlusion, speeding vehicles, vertical or horizontal skew, blurriness, and illumination diversions are possible [145] , the recognition task still stays within a restricted rule set.
Therefore, learning algorithms can be pre-trained, for example, by just going over the alphabet with variations, like changing the font, colors, adding some occlusion, etc. It should be kept in mind that still, vehicular license plate recognition is far from perfect.
In principle, deep learning is trying to mimic the human brain, especially the learning process of a human brain [146] . Equivalent to the fact that we cannot look into someone's brain with its thoughts or mindset, it is also not yet fully understood what is going on inside a deep neural network (even though we have access to all neurons and its connections, in contrast to a human's brain) [147] . Hence, it is as hard to predict exceptions and failures as seen in recent events, like car accidents, as it is to foresee human behavior and mistakes (even if there are ironically deep learning works that try to predict human behavior [148] ). Trained neural networks with several layers and with a few hundred or a few thousand neurons are not understandable anymore in all detail [149] . This stands in strong contrast to pure engineering approaches, like and elevator control that can be understood in every detail. That makes the acceptance of such black box (some even call it Voodoo [150] ) approaches, like deep learning, by the general population much harder.
To conclude, deep learning is an exciting new technique with a lot of potential, but also not free of controversies. We believe that this very first meta-review of medical deep learning reviews and surveys can provide a quick and comprehensive reference for scientists (or just interested readers) who want to get a high-level overview of this field, and maybe want to contribute and thus, accelerate the development in medical deep learning. Hence, the contribution of this meta-review is sixfold:
• providing an overview of current deep learning reviews where a medical application plays the key role,
• arranging the researched works chronologically for a historical red thread and picture over the years,
• extracting the overall number of referenced works and citations to give an impression of the research influence and footprints of the respective field,
• analyzing, exploring and highlighting the main reasons for the massive research efforts on this topic,
• conducting a comprehensive discussion of current state-of-the-art in the deep learning area with achievements but also failures from other domains that should be avoided and not be repeated in the medical area,
• providing a critical expert opinion and pointing out further controversies.