This Research Topic is Volume II of a series. The previous volume can be found here: Advances and Applications of Artificial Intelligence and Numerical Simulation in Risk Emergency Management and Treatment Our world is composed of multidimensional and multifaceted risks. In general, geological, environmental, and ecological risks would exist in both natural and engineering situations, such as karst desertification, water inrush, rock burst, debris flow, and landslide. These risks have great safety threats to human survival. In this regard, risk emergency management and treatment (REMT) has become a pivotal topic addressing the national governance system and its governance capacity. It underlines how to prevent and resolve grand security risks, to timely respond to all kinds of disasters and accidents, as well as to safeguard people’s lives and property and social stability.

In a world where the relentless force of natural and man-made disasters threatens societies, the need for effective disaster management has never been more critical. Predicting Natural Disasters With AI and Machine Learning addresses the challenges of disasters and charts a path toward proactive solutions by applying artificial intelligence (AI) and machine learning (ML). This book begins by interpreting the nature of disasters, clearly distinguishing between natural and man-made hazards. It delves into the intricacies of disaster risk reduction (DRR), emphasizing the human contribution to most disasters. Recognizing the necessity for a multifaceted approach, the book advocates the four ‘R’s - Risk Mitigation, Response Readiness, Response Execution, and Recovery - as integral components of comprehensive disaster management. This book explores various AI and ML applications designed to predict, manage, and mitigate the impact of natural disasters, focusing on natural language processing, and early warning systems. The contrast between weak AI, simulating human intelligence for specific tasks, and strong AI, capable of autonomous problem-solving, is thoroughly examined in the context of disaster management. Its chapters systematically address critical issues, including real-world data handling, challenges related to data accessibility, completeness, security, privacy, and ethical considerations.

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

Artificial intelligence (AI) has shown promise as an effective tool in disaster preparedness and response, providing a unique perspective on some of the most urgent health challenges. Rapid advances in AI technology can potentially revolutionize the way how we respond to emergencies and disasters that affect the world's health, including early warning systems, resource allocation, and real-time decision-making. This Research Topic aims to explore the latest developments in AI and its applications in global health and disaster response, providing a comprehensive overview of the potential and challenges of AI in improving health outcomes in crises. This Research Topic will bring together leading researchers, practitioners, and policymakers in global health and disaster response to share their experiences and insights on how AI can be leveraged to improve response efforts and enhance healthcare delivery.

This book provides the most current and comprehensive overview available today of the critical role of information systems in emergency response and preparedness. It includes contributions from leading scholars, practitioners, and industry researchers, and covers all phases of disaster management - mitigation, preparedness, response, and recovery. 'Foundational' chapters provide a design framework and review ethical issues. 'Context' chapters describe the characteristics of individuals and organizations in which EMIS are designed and studied. 'Case Study' chapters include systems for distributed microbiology laboratory diagnostics to detect possible epidemics or bioterrorism, humanitarian MIS, and response coordination systems. 'Systems Design and Technology' chapters cover simulation, geocollaborative systems, global disaster impact analysis, and environmental risk analysis. Throughout the book, the editors and contributors give special emphasis to the importance of assessing the practical usefulness of new information systems for supporting emergency preparedness and response, rather than drawing conclusions from a theoretical understanding of the potential benefits of new technologies.