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Artificial Intelligence in Healthcare and COVID-19 showcases theoretical concepts and implementational and research perspectives surrounding AI. The book addresses both medical and technological visions, making it even more applied. With the advent of COVID-19, it is obvious that leading universities and medical schools must include these topics and case studies in their usual courses of health informatics to keep up with the pace of technological and medical advancements. This book will also serve professors teaching courses and industry practitioners and professionals working in the R&D team of leading medical and informatics companies who want to embrace AI and eHealth to fight COVID-19. Since AI in healthcare is a comparatively new field, there exists a vacuum of literature in this field, especially when applied to COVID-19. With the area of AI in COVID-19 being quite young, students and researchers usually face a struggle to rely on the few published papers (which are obviously too specific) or whitepapers by tech-giants (which are too wide). Discusses the fundamentals and theoretical concepts of applying AI in healthcare pertaining to COVID-19 Provides a landscape view to the applied aspect of AI in healthcare related COVID-19 through case studies and innovative applications Discusses key concerns and challenges in the field of AI in eHealth during the pandemic, along with other allied fields like IoT, creating a broad platform of transdisciplinary discussion.

Artificial intelligence --- COVID-19 (Disease) --- Medical applications. --- Data processing. --- 2019-nCoV disease --- 2019 novel coronavirus disease --- Coronavirus disease-19 --- Coronavirus disease 2019 --- COVID-19 virus disease --- COVID19 (Disease) --- Novel coronavirus disease, 2019 --- SARS coronavirus 2 disease --- SARS-CoV-2 disease --- Coronavirus infections --- Respiratory infections --- Medicine --- Data processing --- Artificial Intelligence --- COVID-19 --- Medical Informatics Computing