CONTENTS CLASSIFICATION OF HEART ATTACK RISKS WITH ARTIFICIAL INTELLIGENCE METHODS Ezgi DEMİR IMPROVING PATIENT SERVICES IN HEALTHCARE SYSTEMS: OPTIMIZATION OF APPOINTMENT SCHEDULING Uğur ELİİYİ DIGITAL AGE IN HEALTH SERVICES: WEARABLE TECHNOLOGIES, BLOCKCHAIN TECHNOLOGY AND ARTIFICIAL INTELLIGENCE Mehmet ÇİĞDEM – Aygül YANIK THE IMPORTANCE OF ARTIFICIAL INTELLIGENCE IN DIABETES MANAGEMENT Hatice AĞRALI NURSING AND MIDWIFERY IN THE WORLD OF ARTIFICIAL INTELLIGENCE Esma DEMİREZEN THE IMPACT OF MOBILE HEALTH (M-HEALTH) AND ELECTRONIC HEALTH (E-HEALTH) APPLICATIONS ON HEALTH CARE MANAGEMENT Gamze YORGANCIOĞLU TARCAN – Bülent SAPAZ VOS VIEWER ANALYSIS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE AND ECONOMICS Melike TORUN

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

This book focuses on the implementation of Artificial Intelligence in Business, Education and Healthcare, It includes research articles and expository papers on the applications of Artificial Intelligence on Decision Making, Entrepreneurship, Social Media, Healthcare, Education, Public Sector, FinTech, and RegTech. It also discusses the role of Artificial Intelligence in the current COVID-19 pandemic, in the health sector, education, and others. It also discusses the impact of Artificial Intelligence on decision-making in vital sectors of the economy.

This book highlights the analytics and optimization issues in healthcare systems, proposes new approaches, and presents applications of innovative approaches in real facilities. In the past few decades, there has been an exponential rise in the application of swarm intelligence techniques for solving complex and intricate problems arising in healthcare. The versatility of these techniques has made them a favorite among scientists and researchers working in diverse areas. The primary objective of this book is to bring forward thorough, in-depth, and well-focused developments of hybrid variants of swarm intelligence algorithms and their applications in healthcare systems.

The application of Artificial Intelligence (AI) in the healthcare sector is certain to boost levels of automation and productivity but, paradoxically, it will also increase the availability of “first line competence.” At the same time as demographic trends are affecting demand for health and social care, the technological developments we are seeing make it highly likely that AI will play a decisive role in tackling the challenges our healthcare systems will encounter. This book reveals systemic connections to tackle questions about the potential impact of AI on future challenges in the healthcare sector. Specifically, it develops practical proposals for ways in which AI can be applied to solve these forthcoming issues. It emphasizes the importance of AI in what is known in the literature as human augmentation. The book’s innovative perspective is apparent in the way it challenges conventional wisdom in the context of several pressing questions, such as: • What opportunities and challenges could arise from the application of AI in the healthcare sector? • How can the philosophy of medicine, viewed from a systemic perspective, help us to understand, explain, and resolve some of the future challenges in the healthcare sector? • How could AI affect inclusive employment opportunities for people with disabilities? The book also contains an underlying argument to the effect that the rational approach adopted by economists is perhaps less rational when applied to a healthcare sector that is crying out for more “first line competence.” The primary readership will be academic, but the book will also appeal to policymakers, consultants, HR departments, healthcare stakeholders, and related practitioners.

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.