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Novel coronavirus (COVID-19) pandemic, has raised a serious situation across world human population and has become serious threat as contagious outbreak. This paper aims to overview the recently intelligent systems based on Artificial Intelligence using different medical imaging modalities like Computer Tomography (CT) and X-ray. This paper specifically discusses the machine learning techniques developed for COVID-19 diagnosis and provides insights on well-known data sets used to train these AI based networks. It also highlights the use of AI in COVID detection and classification at faster process where normal COVID testing takes couple of days to produce. Finally, we conclude by addressing the challenges associated with the use of Machine Leaming methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways machine learning techniques are used and how they potentially further work in com batting the outbreak of CO VID-19. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges. [ABSTRACT FROM AUTHOR]

Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection. [ABSTRACT FROM AUTHOR]