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As the demand for advanced technologies to revolutionize patient care intensifies, the medical industry faces a pressing need to confront challenges hindering the assimilation of AI-enhanced healthcare systems. Issues such as data interoperability, ethical considerations, and the translation of AI advancements into practical clinical applications pose formidable hurdles that demand immediate attention. It is within this context of challenges and opportunities that the book, Clinical Practice and Unmet Challenges in AI-Enhanced Healthcare Systems promises to pave the way for a transformative era in healthcare. The book serves as a comprehensive guide for academic scholars, researchers, and healthcare professionals navigating the dynamic landscape of data-driven, AI-enhanced healthcare. By showcasing the latest advancements, the book empowers its readers to not only comprehend the existing frontiers in data sciences and healthcare technologies but also to actively contribute to overcoming obstacles. Through detailed case studies and practical guidance, the publication equips its audience with the skills necessary to implement AI in various clinical settings.
This volume will present critical and comphrehensive reviews examining the latest research and developments in nanoscience in accessible articles. Quantum dot synthesis, soft lithography and graphene will feature in the debut volume, along with perspectives on research in China and India.
Artificial Intelligence (AI) has rapidly emerged as a revolutionary force across various sectors, with a profound influence permeating the domain of higher education. AI in higher education encompasses a wide range of applications designed to enhance teaching methodologies, streamline administrative processes, and personalize learning experiences. The transformative potential of AI lies in its ability to process vast amounts of data, identify patterns, and make intelligent decisions, which can significantly improve educational outcomes. AI-Driven Learning and Engagement in Higher Education provides a comprehensive exploration of these themes and offers insights into the theoretical foundations, practical applications, and ethical implications of AI in education. Each chapter delves into specific aspects of AI integration, from personalized learning and intelligent tutoring systems to administrative automation and ethical considerations. Covering topics such as applied artificial intelligence, online learning, and student success, this book is an excellent resource for educators, administrators, policymakers, researchers, academicians, and more.
Nonlinear problems, originating from applied science that is closely related to practices, contain rich and extensive content. It makes the corresponding nonlinear models also complex and diverse. Due to the intricacy and contingency of nonlinear problems, unified mathematical methods still remain far and few between. In this regard, the comprehensive use of symmetric methods, along with other mathematical methods, becomes an effective option to solve nonlinear problems.