The disconnect between technology and traditional practices poses a significant challenge. Many healthcare professionals and individuals struggle to navigate the influx of emerging technologies, hindering the full realization of their potential in revolutionizing health literacy and medical practice. The lack of cohesive understanding and integration of technologies like mobile applications, wearable devices, artificial intelligence, and telemedicine impedes the seamless delivery of healthcare services and obstructs individuals from actively managing their health. Transformative Approaches to Patient Literacy and Healthcare Innovation offers a comprehensive solution to bridge the gap between healthcare and technology. Delving into the dynamic fusion of these domains, it unravels the transformative power of technology applications, showcasing how they enhance health literacy and empower individuals to make informed decisions about their well-being. By providing insights into the integration of mobile health apps, electronic health records, extended reality, artificial intelligence, and more, the book equips readers with the knowledge needed to navigate the evolving healthcare landscape with confidence.

Technology-Enhanced Healthcare Education promotes the best practices and lessons learnt from COVID-19 and highlights the importance and impact of using information systems to increase levels of health literacy.

Organizations face a pressing challenge in today's rapidly evolving economies: navigating the ethical complexities of adopting Artificial Intelligence (AI) and related technologies. As AI becomes increasingly integral to operations, transparency, fairness, accountability, and privacy concerns are more critical than ever. Organizations need practical guidance to develop and implement AI ethics strategies effectively. Cases on AI Ethics in Business offers a comprehensive solution by examining AI Ethics through theoretical lenses and innovative practices. It provides a roadmap for organizations to address ethical challenges in AI adoption, offering insights from leaders in the field. With a focus on theory-to-practice, the book equips readers with actionable strategies and frameworks to navigate the ethical implications of AI, ensuring responsible and sustainable AI deployment.

In our rapidly evolving digital landscape, the threat of natural disasters looms large, necessitating innovative solutions for effective disaster management. Integrating Artificial Intelligence (AI) and the Internet of Things (IoT) presents a transformative approach to addressing these challenges. However, despite the potential benefits, the field needs more comprehensive resources that explore the full extent of AI and IoT applications in disaster management. AI and IoT for Proactive Disaster Management fills that gap by examining how AI and IoT can revolutionize disaster preparedness, response, and recovery. It offers a deep dive into AI frameworks, IoT infrastructures, and the synergy of these technologies in predicting and managing natural disasters. Ideal for undergraduate and postgraduate students, academicians, research scholars, industry professionals, and technology enthusiasts, this book serves as a comprehensive guide to understanding the intersection of AI, IoT, and disaster management. By showcasing cutting-edge research and practical applications, this book equips readers with the knowledge and tools to harness AI and IoT for more efficient and effective disaster management strategies.

Integrating electric vehicles (EVs) into power distribution systems presents significant challenges, particularly concerning power source dependability and grid stability. The distribution system, a critical element of the power system, is susceptible to failures and power outages exacerbated by the extensive adoption of EVs. Additionally, managing the administration, monitoring, and control of power systems in the context of EV integration is a complex and daunting task for energy experts. A Sustainable Future with E-Mobility: Concepts, Challenges, and Implementations offers a comprehensive solution to these challenges. It explores infrastructure frameworks, planning strategies, control strategies, and software applications for integrating EVs with power distribution systems, focusing on innovative grid developments. By providing insights into architectural reconfiguration, restoration strategies, power quality control, and regulatory aspects, the book equips students, researchers, academicians, policymakers, and industry experts with the knowledge needed to achieve a secure, resilient, and efficient integration of EVs into distribution networks.

The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI.

The world of pharmaceutical research is moving at lightning speed, and the age-old approach to drug discovery faces many challenges. It's a fascinating time to be on the cutting edge of medical innovation, but it's certainly not without its obstacles. The process of developing new drugs is often time-consuming, expensive, and fraught with uncertainty. Researchers are constantly seeking ways to streamline this process, reduce costs, and increase the success rate of bringing new drugs to market. One promising solution lies in the convergence of pharmacy science and engineering, particularly in computational drug discovery. Converging Pharmacy Science and Engineering in Computational Drug Discovery presents a comprehensive solution to these challenges by exploring the transformative synergy between pharmacy science and engineering. This book demonstrates how researchers can expedite the identification and development of novel therapeutic compounds by harnessing the power of computational approaches, such as sophisticated algorithms and modeling techniques. Through interdisciplinary collaboration, pharmacy scientists and engineers can revolutionize drug discovery, paving the way for more efficient and effective treatments. This book is an invaluable resource for pharmaceutical scientists, researchers, and engineers seeking to enhance their understanding of computational drug discovery. This book inspires future innovations by showcasing cutting-edge methodologies and innovative research at the intersection of pharmacy science and engineering. It contributes to the ongoing evolution of pharmaceutical research. It offers practical insights and solutions that will shape the future of drug discovery, making it essential reading for anyone involved in the pharmaceutical industry.