In a world characterized by complexity and rapid change, the intersection of healthcare, social sciences, and technology presents a formidable challenge. The vast array of interconnected issues, ethical dilemmas, and technological advancements often evade comprehensive understanding within individual disciplines. The problem lies in the siloed approach to these critical domains, hindering our ability to navigate the complexities of our modern world effectively. Change Dynamics in Healthcare, Technological Innovations, and Complex Scenarios emerges as a transformative solution, offering a beacon of insight and knowledge to those grappling with the intricate dynamics of our interconnected society. Change Dynamics in Healthcare, Technological Innovations, and Complex Scenarios dives into organizational narratives, ethical challenges, and technological promises across healthcare, social sciences, and technology. It doesn't merely acknowledge the interplay between these disciplines; it celebrates their interconnectedness. By dissecting, analyzing, and synthesizing critical developments, this book serves as a compass, providing a rich resource for comprehending the multifaceted impacts of emerging changes.

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

Picture a world where autonomous systems operate continuously and intelligently, utilizing real-time data to make informed decisions. Such systems have the potential to revolutionize agriculture, urban infrastructure, and industrial automation. This transformation, often termed the Internet of Self-Sustaining Systems (IoSS), is a pivotal topic that demands academic attention and exploration. Addressing this critical issue head-on is The Convergence of Self-Sustaining Systems With AI and IoT, which offers an in-depth examination of this transformative convergence. It serves as a guiding light for academic scholars seeking to unravel the vast potential of self-sustaining systems coupled with AI and IoT. Inside its pages, readers will delve into AI-driven autonomous agriculture, eco-friendly transportation solutions, and intelligent energy management. Moreover, the book explores emerging technologies, security concerns, ethical considerations, and governance frameworks. Join us on this intellectual journey and position yourself at the forefront of the AI and IoT revolution that promises a sustainable, autonomous future.

The current healthcare system faces complications including data acquisition, interpretation, and delivery challenges, particularly in out-of-hospital scenarios. The shortage of medical resources intensifies the demand for efficient information gathering and processing. Moreover, the potential of pervasive computing still needs to be explored in healthcare, limiting the industry's ability to leverage innovations like artificial intelligence, augmented reality, and virtual reality. Ubiquitous Computing and Technological Innovation for Universal Healthcare addresses the medical field's critical challenges. It presents innovative solutions grounded in the marriage of Unmanned Aerial Vehicles (UAV), pervasive computing, and metaverse intelligence. It outlines how these UAVs redefine out-of-hospital care, addressing the pressing need for efficient data collection and interpretation amid a global medical shortage. Integrating cognitive algorithms is explored to automate diagnosis and enhance healthcare systems' emergency responsiveness. The book revolves around developing and integrating treatment programs leveraging UAV communication. Topics such as artificial intelligence, telemedicine, blockchain, digital twins, augmented reality, and virtual reality are delved into for their role in creating intelligent healthcare systems. The focus on rapid identification of underlying health issues, real-time monitoring in the metaverse, and the economic, social, and environmental impact of these systems adds depth to the discourse. Structured as a vital resource for researchers, academicians, industry professionals, policy-makers, and system designers, this book bridges the gap between theory and application.

"This book is a compendium that celebrates the profound value of interconnected academic fields and the transformative impact of applied research in healthcare, social sciences, organizational behavior, and technology"--

The integration of artificial intelligence (AI) stands as both a promise and a challenge in the field of healthcare. As technological advancements reshape the industry, academic scholars find themselves at the forefront of a crucial dialogue about the ethical implications and societal repercussions of AI. The accelerating sophistication of AI technologies brings forth a central dilemma: how to maintain the crucial human touch required for compassionate and effective patient care in the face of unprecedented technical progress. This challenge is not only a theoretical concern but a pressing reality as healthcare systems increasingly rely on AI-driven solutions. Approaches to Human-Centered AI in Healthcare emerges as a significant guide, offering a comprehensive exploration of the opportunities and challenges entwined with the integration of AI into healthcare. The book becomes a critical compass, navigating readers through the intricate intersections of AI and patient care. By delving into real-world case studies, cutting-edge research findings, and practical recommendations, it provides a roadmap for scholars to navigate the complexities of healthcare AI. In doing so, it aims not only to inform but to shape the discourse around the responsible integration of AI, ensuring that the fundamental principles of compassionate patient care remain at the forefront.

In the field of pharmaceutical sciences, the integration of artificial intelligence (AI) has emerged as a groundbreaking force, propelling the field into uncharted territories of discovery and innovation. As traditional approaches in drug discovery and development encounter new challenges, the need for cutting-edge technologies becomes increasingly apparent. AI-Powered Advances in Pharmacology offers an insightful exploration of this critical intersection between AI and pharmacological research. This book delves into how AI technologies are reshaping the understanding of diseases, predicting drug responses, and optimizing therapeutic interventions. It navigates through the relationship between AI algorithms, big data analytics, and traditional pharmacological methodologies, promising to accelerate drug development and usher in a new era of precision medicine. The primary objective of AI-Powered Advances in Pharmacology is to conduct a thorough exploration of the integration of artificial intelligence (AI) into pharmacological research, shedding light on its transformative impact on drug discovery, development, and personalized medicine. This comprehensive overview aims to serve as a valuable resource for researchers, practitioners, and students in the field, bridging the gap between traditional pharmacological approaches and AI methodologies. Through case studies and discussions of emerging trends, the book contributes to the evolving landscape of pharmacology, fostering a deeper understanding of diseases, optimizing therapeutic interventions, and shaping the future of precision medicine. By providing practical insights, it aims to inspire further advancements at the intersection of artificial intelligence and pharmacology.