## 4 Discussion  
The worldwide most available radiographic method to explore lung lesions is still the X-ray examination [38]. In addition, hospitalized patients in intensive care units with suspected COVID-19 pneumonia usually cannot be transported to the radiological centers in the same hospital, however, an X-ray image examination can routinely be performed on the bed of patients. Herein, we detailed the development of a new Inception-V3 based CNN system to support the identification of COVID-19 pneumonia using a chest radiograph. We examined the performance of the algorithms using a dataset from patients treated by a hospital in Espirito Santo, Brazil, during an acute phase of the pandemic and compared it with one previously published algorithm. This study validated in a controlled dataset that the two AI algorithms, Cimatec_XCOV19 and DeepCOVID-XR have, respectively, a specificity of 0.82 and 0.94, a sensitivity of 0.85 and 0.77, and an AUC ROC of 0.92 and 0.97. The performance of both algorithms is good enough to consider them reasonable tools for supporting COVID-19 pneumonia screening. The models generated too many false positives, reinforcing the limitations of the AI systems as a sole diagnostic tool for COVID-19. The generalization of different datasets is a known problem in AI [39]. This result also reinforces the need for better techniques to adapt the algorithm to the characteristics of new datasets. Advances in the performance of both algorithms might foster the adoption of such systems in scale. In order to facilitate future works and support the development of new AI algorithms in this area, we made all the code freely available [34]. The external validation dataset with labels is also publicly available. They are a good source of images for testing and training new algorithms. The algorithm serves well for educational purposes. We believe that medical staff, under intense work pressure in a pandemic situation, can use the algorithm to help fast screening of COVID-19 cases. One limitation of this study was the age of the population in the external validation dataset. All patients were older than 50 years and the average age was over 72 years. On one hand, this may limit the ability of the model to extrapolate the analysis to different age groups. Some patients had previous alterations in the chest, though with normal diagnosis. This might represent a bias and could lead to some misclassification of the AI algorithms. Despite this, when we consider that elderly people can be more impacted by COVID-19, these results show that these solutions can be of great help during new COVID-19 pandemic emergences. Furthermore, all of this knowledge, methodology, and source code can be easily applied and adapted to new eventual pandemic situations, by using transfer learning with new data from CXR exams. The importance of CXR exams is evident as an alternative for supporting COVID-19 fast screening, especially to identify severe cases, as there might be no findings on CXR exams in mild or early-stage COVID-19 patients. AI algorithms can support the detection of pneumonia caused by COVID-19 in chest radiographs, as they are fast, simple, cheap, safe, and a ubiquitous tool for the management of COVID-19 patients. In the absence of a radiologist specialist, Cimatec_XCOV19 and DeepCOVID-XR AI systems might be good tools to support the detection of COVID-19. Future studies should explore other freely available AI models, test new feature extraction techniques, and use the indications of Grad-CAM and other explainable AI techniques to understand and enhance the actual classification algorithms. Cimatec_XCOV19 is now under controlled testing in a hospital in Espirito Santo, Brazil. Feedback from clinical practice will be paramount to evolving the algorithm and mitigating adoption risks.