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.

This Research Topic is the fourth volume of the series Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine Volume I: Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume I Volume II:Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume II Volume III:Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume III Analytics based on artificial intelligence has greatly advanced scientific research fields like natural language processing and imaging classification. Clinical research has also greatly benefited from artificial intelligence. Emergency and critical care physicians face patients with rapidly changing conditions, which require accurate risk stratification and initiation of rescue therapy. Furthermore, critically ill patients, such as those with sepsis, acute respiratory distress syndrome, and trauma, are comprised of heterogeneous population. The “one-size-fit-all” paradigm may not fit for the management of such heterogeneous patient population. Thus, artificial intelligence can be employed to identify novel subphenotypes of these patients. These sub classifications can provide not only prognostic value for risk stratification but also predictive value for individualized treatment. With the development of transcriptome providing a large amount of information for an individual, artificial intelligence can greatly help to identify useful information from high dimensional data. Altogether, it is of great importance to further utilize artificial intelligence in the management of critically ill patients.

This handbook on Artificial Intelligence (AI) in healthcare consists of two volumes. The first volume is dedicated to advances and applications of AI methodologies in specific healthcare problems, while the second volume is concerned with general practicality issues and challenges and future prospects in the healthcare context. The advent of digital and computing technologies has created a surge in the development of AI methodologies and their penetration to a variety of activities in our daily lives in recent years. Indeed, researchers and practitioners have designed and developed a variety of AI-based systems to help advance health and well-being of humans. In this first volume, we present a number of latest studies in AI-based tools and techniques from two broad categories, viz., medical signal, image, and video processing as well as healthcare information and data analytics in Part 1 and Part 2, respectively. These selected studies offer readers practical knowledge and understanding pertaining to the recent advances and applications of AI in the healthcare sector.

While medical specialists in disaster mitigation, preparedness, and response are needed worldwide, the initial phase of disaster response is almost entirely dependent upon local resources—making it essential that all healthcare personnel have a working knowledge of the field and stand ready to integrate into the response system. Ciottone's Disaster Medicine, 3rd Edition, is the most comprehensive reference available to help accomplish these goals in every community. It thoroughly covers isolated domestic events as well as global disasters and humanitarian crises. Dr. Gregory Ciottone and more than 200 worldwide authorities share their knowledge and expertise on the preparation, assessment, and management of both natural and man-made disasters, including lessons learned by the responders to contemporary disasters such as the COVID-19 pandemic, Australian and western U.S. wildfires, European heatwaves, the Beirut explosion, recent hurricanes and typhoons, and the global refugee crisis. Part 1 offers an A-to-Z resource for every aspect of disaster medicine and management, while Part 2 features an exhaustive compilation of every conceivable disaster event, organized to facilitate quick reference in a real-time setting. Covers basic concepts such as identification of risks, organizational preparedness, equipment planning, disaster education and training, and more advanced concepts such as disaster risk reduction, health in complex emergencies, building local disaster resiliency, psychological impact of disasters on children, and more. Contains new decision trees throughout that help guide you through the decision-making process in difficult situations. Uses an easy-to-follow, templated approach to historical perspectives, overviews of current practice including pre-incident and post-incident actions, medical treatment of casualties, and potential pitfalls. Includes updated sections on man-made disasters, including mass casualties, active shooter situations, integrated response to terrorist attacks, and chemical/biological/radiological/nuclear/high-yield explosives disasters. Discusses the latest technologies, such as the use of mobile disaster applications, drone response systems, and virtual reality simulation training. Features thoroughly updated information on crisis leadership, practical applications of disaster epidemiology, disaster and climate change, and the integration of non-government agencies (NGOs) in disaster response—a critical topic for those responding to humanitarian needs overseas. Includes new chapters on Pandemic Preparedness and Response, Disaster Medicine in a Changing Climate, Disaster Response in Asia, Building Local Capacity and Disaster Resiliency, Civilian-Military Coordination in Disaster Response, Medical Simulation in Disaster Preparedness, Disaster Nursing, Crisis Meta-Leadership, Palliative Care in Disasters, Counter-Terrorism Medicine, SARS CoV (COVID-19 and SARS), and Disasters in Space Travel.