Accurate estimation, diagnosis, and prevention of COVID-19 is a global challenge for healthcare organizations. Innovative measures can introduce and implement AI, and Mathematical Modeling applications. This book provides insight into the recent advances of applications, statistical methods, and mathematical modeling for the healthcare industry.

This book covers the state-of-the-art applications of AI and Machine Learning in past epidemics, pandemics, and COVID-19. It offers recent global case studies, and discusses how AI and statistical methods, initiatives, and applications such as Machine Learning, Deep Learning, Correlation and Regression Analysis play a major role in the prediction, diagnosis, and prevention of a pandemic. It will also focus on how AI and statistical applications can facilitate and restructure the healthcare system.

This book is written for Researchers, Students, Professionals, Executives, and the general public.

Accurate estimation, diagnosis, and prevention of COVID-19 is a global challenge for healthcare organizations. Innovative measures can introduce and implement AI, and Mathematical Modeling applications. This book provides insight into the recent advances of applications, statistical methods, and mathematical modeling for the healthcare industry.

This book covers the state-of-the-art applications of AI and Machine Learning in past epidemics, pandemics, and COVID-19. It offers recent global case studies, and discusses how AI and statistical methods, initiatives, and applications such as Machine Learning, Deep Learning, Correlation and Regression Analysis play a major role in the prediction, diagnosis, and prevention of a pandemic. It will also focus on how AI and statistical applications can facilitate and restructure the healthcare system.

This book is written for Researchers, Students, Professionals, Executives, and the general public.

Artificial intelligence (AI) in medicine is rising, and it holds tremendous potential for more accurate findings and novel solutions to complicated medical issues. Biomedical AI has potential, especially in the context of precision medicine, in the healthcare industry’s next phase of development and advancement. Integration of AI research into precision medicine is the future; however, the human component must always be considered.

Explainable Artificial Intelligence in Medical Imaging: Fundamentals and Applications focuses on the most recent developments in applying artificial intelligence and data science to health care and medical imaging. Explainable artificial intelligence is a well-structured, adaptable technology that generates impartial, optimistic results. New healthcare applications for explicable artificial intelligence include clinical trial matching, continuous healthcare monitoring, probabilistic evolutions, and evidence-based mechanisms. This book overviews the principles, methods, issues, challenges, opportunities, and the most recent research findings. It makes the emerging topics of digital health and explainable AI in health care and medical imaging accessible to a wide audience by presenting various practical applications.

Presenting a thorough review of state-of-the-art techniques for precise analysis and diagnosis, the book emphasizes explainable artificial intelligence and its applications in healthcare. The book also discusses computational vision processing methods that manage complicated data, including physiological data, electronic medical records, and medical imaging data, enabling early prediction. Researchers, academics, business professionals, health practitioners, and students all can benefit from this book’s insights and coverage.

Accurate estimation, diagnosis, and prevention of COVID-19 is a global challenge for healthcare organizations. Innovative measures can introduce and implement AI, and Mathematical Modeling applications. This book provides insight into the recent advances of applications, statistical methods, and mathematical modeling for the healthcare industry.

This book covers the state-of-the-art applications of AI and Machine Learning in past epidemics, pandemics, and COVID-19. It offers recent global case studies, and discusses how AI and statistical methods, initiatives, and applications such as Machine Learning, Deep Learning, Correlation and Regression Analysis play a major role in the prediction, diagnosis, and prevention of a pandemic. It will also focus on how AI and statistical applications can facilitate and restructure the healthcare system.

This book is written for Researchers, Students, Professionals, Executives, and the general public.

Accurate estimation, diagnosis, and prevention of COVID-19 is a global challenge for healthcare organizations. Innovative measures can introduce and implement AI, and Mathematical Modeling applications. This book provides insight into the recent advances of applications, statistical methods, and mathematical modeling for the healthcare industry.

This book covers the state-of-the-art applications of AI and Machine Learning in past epidemics, pandemics, and COVID-19. It offers recent global case studies, and discusses how AI and statistical methods, initiatives, and applications such as Machine Learning, Deep Learning, Correlation and Regression Analysis play a major role in the prediction, diagnosis, and prevention of a pandemic. It will also focus on how AI and statistical applications can facilitate and restructure the healthcare system.

This book is written for Researchers, Students, Professionals, Executives, and the general public.

Artificial intelligence (AI) in medicine is rising, and it holds tremendous potential for more accurate findings and novel solutions to complicated medical issues. Biomedical AI has potential, especially in the context of precision medicine, in the healthcare industry’s next phase of development and advancement. Integration of AI research into precision medicine is the future; however, the human component must always be considered.

Explainable Artificial Intelligence in Medical Imaging: Fundamentals and Applications focuses on the most recent developments in applying artificial intelligence and data science to health care and medical imaging. Explainable artificial intelligence is a well-structured, adaptable technology that generates impartial, optimistic results. New healthcare applications for explicable artificial intelligence include clinical trial matching, continuous healthcare monitoring, probabilistic evolutions, and evidence-based mechanisms. This book overviews the principles, methods, issues, challenges, opportunities, and the most recent research findings. It makes the emerging topics of digital health and explainable AI in health care and medical imaging accessible to a wide audience by presenting various practical applications.

Presenting a thorough review of state-of-the-art techniques for precise analysis and diagnosis, the book emphasizes explainable artificial intelligence and its applications in healthcare. The book also discusses computational vision processing methods that manage complicated data, including physiological data, electronic medical records, and medical imaging data, enabling early prediction. Researchers, academics, business professionals, health practitioners, and students all can benefit from this book’s insights and coverage.