## 1 Introduction
Despite many algorithms being available for public use, there are still many obstacles to their wide application in clinical practice. A study published in _Nature Machine Intelligence_[16] systematically reviewed publications of machine learning models for the diagnosis or prognosis of COVID-19 from X-ray or CT images that were published between 1 January 2020 and 3 October 2020. The search identified 2212 studies, of which 415 were included after initial screening, and, after a more rigorous quality screening, 62 studies were included in the systematic review. The conclusion is impressive. None of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. Our review also identified fundamental problems that limit the adoption of algorithms in clinical practice. The source code and the training and testing data are rarely publicly available. It is not possible to replicate the results and evaluate the AI algorithm on different datasets. We noticed that usually, this happens because patient data protection policiesprevent the release of data or because there are commercial interests in the developed software tool. Sometimes the researchers provide only part of the source code. In addition, most studies used a limited number of images from local sources and, therefore, their models may not generalize well to other phenotypes and geographic regions' contexts. Many works used unreliable public datasets for training, did not provide external validation or presented deficient model robustness metrics. Our observations are in line with the findings identified in the studies of [16; 17; 18]. Table 1 presents the open-source algorithms published in the major peer-reviewed publications to the best of our knowledge. Only two other studies used datasets larger than 25,000 chest X-ray images (CXR) for training, and only one had more than 2000 COVID-19 cases. We avoided repeating the most common flaws identified in the available studies. We carefully prepared and used a large and multi-centric dataset for training the algorithm. We used an external validation dataset with data carefully labeled by two experienced radiologists and benchmarked our algorithm with a well-known algorithm on the same dataset. We sought to not only validate the hypothesis that supervised AI algorithms applied to chest radiographs can be an alternative for supporting COVID-19 detection, but also to share all the details related to the major methodological decisions taken to develop our proposed solution, providing full access to the code and a valuable annotated external test dataset. Thus, the main contributions of our work are:  
* The proposal of a new DL system based on the Inception V3 architecture, one that supports the identification of normal and abnormal CXR examinations and the diagnosis of COVID-19. * The preparation and publication of an annotated CXR dataset with 1158 images. It is an external validation dataset suitable not only for this but also for future works. * The evaluation of the classification metrics of our algorithm in an external validation dataset and a comparison of the performance with a state-of-art algorithm. * The guarantee of reproducibility.