Abstract Background: Diagnostic imaging is regarded as funda-mental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Aim of Study: To evaluate diagnostic accuracy of conven-tional radiography (CXR) using deep learning (DL) algorithms for the detection of pneumonia in COVID-19 patients and comparing findings with CT chest. Subjects and Methods: This study was retrospective study conducted at Radiology Department, Ain Shams University from November 2020 till the end of the study. Results: There was significant direct proportional between both grades of CT and AI score as p-value was (<0.05). The sensitivity was more in AI, while specificity was more in x-ray using CT consolidations as a reference to assess. Conclusion: AI, applied to the interpretation of radiological images, allows to streamline and improve diagnosis while optimising the workflow of radiologists. Despite its low sensitivity compared to CT, efforts to improve the diagnostic yield of CXR are of the utmost interest, since it is the most common and widely used imaging method. Used as support in clinical practice and, in conjunction with other diagnostic techniques, it could help increase efficiency in the management of the COVID-19 infection.