An Overview of Mapping the Landscape of AI Applications Against SARS-Cov-2 Virus

declared an epidemic by the globe Health Organization, that has according over eighteen million confirmed cases as of August five, 2020. during this review, we have a tendency to gift an outline of recent studies exploitation Machine Learning and, a lot of generally, AI, to tackle several aspects of the COVID19 crisis. With the continued growth of the COVID-19 pandemic, researchers worldwide area unit working to higher perceive and suppress its unfold. Key areas of analysis embody studyingCOVID-19 transmission, facilitating its detection, developing doable vaccines and treatments, and understanding the socio-economic impacts of the pandemic. during this article, we discuss however AI (AI) will contribute to those goals by enhancing in progress [1]. When it involves medical imaging, associate degree AI model could perform bound tasks, like reading CT respiratory organ scans, faster and, given the correct knowledge to coach on, even a lot of accurately than a medical skilled. With this pandemic, fast medicine exploitation machine learning (ML) approaches may save lives. In many promising studies, AI models were trained to spot potential COVID-19 cases; others area unit combining ready-made code with custom machine learning approaches; others area unit employing a human-in-the-loop approach to cut back the time needed to label the sickness. All of those efforts area unit in associate degree inchoate section, however, the preliminary results area unit actually encouraging [2]. How AI will inform medical analysis against COVID-19. When it involves medical imaging, associate degree AI model could perform bound tasks, like reading CT respiratory organ scans, faster and, given the correct knowledge to coach on, even a lot of accurately than a medical skilled. With this pandemic, fast medicine exploitation machine learning (ML) approaches may save lives. In many promising studies, AI models were trained to spot potential COVID-19 cases; others area unit combining ready-made code with custom machine learning approaches; others area unit employing a human-in-the-loop approach to cut back the time needed to label the sickness.