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 example-a 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.