This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an examplea truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions.

Covers the state-of-the-art AI and machine learning (ML) tools for public health care in resource-constrained regions Includes challenges and opportunities especially when we consider social factors Serves as a resource for diverse audience: computer scientists, healthcare professionals, and social sciences

Autorentext

Professor KC Santosh, Ph.D., is Chair of the Department of Computer Science at the University of South Dakota (USD). He also serves International Medical University as an Adjunct Professor (Full). Before joining USD, he worked as Research Fellow at the US National Library of Medicine (NLM), National Institutes of Health (NIH). He was Postdoctoral Research Scientist at the Loria Research Centre (with industrial partner, ITESOFT (France)). He has demonstrated expertise in artificial intelligence, machine learning, pattern recognition, computer vision, image processing, and data mining with applications- such as medical imaging informatics, document imaging, biometrics, forensics and speech analysis. His research projects are funded (of more than $2m) by multiple agencies, such as SDCRGP, Department of Education, National Science Foundation, and Asian Office of Aerospace Research and Development. He is the proud recipient of the Cutler Award for Teaching and Research Excellence (USD,2021), the President's Research Excellence Award (USD, 2019), and the Ignite from the U.S. Department of Health & Human Services (2014).
Professor Loveleen Gaur, Ph.D., is Professor and Program Director, Artificial Intelligence & Business Intelligence and Data Analytics of the Amity International Business School, Amity University, Noida, India. She is Senior IEEE Member and Series Editor with CRC and Wiley. She has significantly contributed to enhancing scientific understanding by participating in over three hundred scientific conferences, symposia, and seminars, by chairing technical sessions and delivering plenary and invited talks. She has specialized in the fields of artificial intelligence, machine learning, pattern recognition, Internet of things, data analytics, and business intelligence. She has chaired various positions in the international conferences of repute and is Reviewer with top-rated journals of IEEE, SCI, and ABDC Journals. She has been honored with prestigious national and international awards. She is also actively involved in various reputed projects of Government of India and abroad.

Inhalt
Introduction to AI in Public Health.- Open Public Health Issues.- AI Solutions to Public Health Issues.- AI in Sustainable Public Healthcare.- AI in Precision Medicine.- Societal Impact: AI in Public Health Issues.- Case Studies - AI for Infectious Disease.- Privacy, Security and Ethical Issues.