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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.
Publisher : Springer
Publication date : November 27, 2021
Edition : 1st ed. 2021
Language : English
Print length : 100 pages
ISBN-10 : 9811667675
ISBN-13 : 978-9811667671
Item Weight : 5.9 ounces
Dimensions : 6.1 x 0.23 x 9.25 inches
Part of series : SpringerBriefs in Applied Sciences and Technology
Best Sellers Rank: #3,433,636 in Books (See Top 100 in Books) #614 in Medical Informatics (Books) #1,259 in Artificial Intelligence (Books) #1,294 in Public Health (Books)
