Akila Sarirete – författare

Innovation in Health Informatics: A Smart Healthcare Primer explains how the most recent advances in information and communication technologies have paved the way for new breakthroughs in healthcare. The book showcases current and prospective applications in a context defined by an imperative to deliver efficient, patient-centered and sustainable healthcare systems. Topics discussed include big data, medical data analytics, artificial intelligence, machine learning, virtual and augmented reality, 5g and sensors, Internet of Things, nanotechnologies and biotechnologies. Additionally, there is a discussion on social issues and policy- making for the implementation of smart healthcare.

This book is a valuable resource for undergraduate and graduate students, practitioners, researchers, clinicians and data scientists who are interested in how to explore the intersections between bioinformatics and health informatics.

Provides a holistic discussion on the new landscape of medical technologies, including big data, analytics, artificial intelligence, machine learning, virtual and augmented reality, 5g and sensors, Internet of Things, nanotechnologies and biotechnologies Presents a case study driven approach, with references to real-world applications and systems Discusses topics with a research-oriented approach that aims to promote research skills and competencies of readers

Artificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate.

The book is split into five sections: big data infrastructure, framework and design for smart healthcare; signal processing techniques for smart healthcare applications; business analytics (descriptive, diagnostic, predictive and prescriptive) for smart healthcare; emerging tools and techniques for smart healthcare; and challenges (security, privacy, and policy) in big data for smart healthcare. The content is carefully developed to be understandable to different members of healthcare chain to leverage collaborations with researchers and industry.

Presents a holistic discussion on the new landscape of data driven medical technologies including Big Data, Analytics, Artificial Intelligence, Machine Learning, and Precision Medicine Discusses such technologies with case study driven approach with reference to real world application and systems, to make easier the understanding to the reader not familiar with them Encompasses an international collaboration perspective, providing understandable knowledge to professionals involved with healthcare to leverage productive partnerships with technology developers

Innovation in Health Informatics: Driving Value-Based Healthcare through Economic Efficiency, Second Edition addresses rapid advancements in health informatics by integrating the latest technologies and methodologies. The revised edition includes new chapters on Digital Twins, Blockchain, AI Applications for Personalized Medicine, and Health Analytics Dashboards, enriching foundational topics. The book delves into the economic implications and practical applications of these technologies, offering evidence-based strategies for their integration into healthcare systems. For the academic audience, this book bridges the gap between theory and practice, featuring practical case studies, end-of-chapter summaries, and interactive components like QR codes linking to online resources and simulation videos.The primary goals of this revision are to update the book with the latest technological advancements, expand the scope to include emerging topics, and enhance existing chapters, ensuring it remains a comprehensive resource for professionals and students. This updated edition equips readers with up-to-date knowledge and tools to navigate and innovate within the evolving landscape of health informatics.Includes new chapters on cutting-edge topics such as Digital Twins, Blockchain, AI applications for Personalized Medicine, and Health Analytics DashboardsProvides actionable insights and real-world applications, making complex concepts accessible and practical for implementation through updated case studies, practical examples, and interactive components like QR codesEquips readers with the knowledge and strategies needed to stay ahead in the rapidly evolving field of health informatics, thus ensuring relevance across various regions and contexts

Responsible AI in Healthcare: Navigating the EU AI Act and Global Regulatory Frameworks examines how health systems design, deploy, and oversee AI under evolving legal and ethical mandates. It provides an integrated, practice-oriented roadmap for compliance and responsible innovation. This book offers the first integrated, comparative treatment of the EU AI Act and global regulatory frameworks for healthcare, translating ethics and law into operational evaluation, governance, and value assessment. Through structured cases, decision tools, and stakeholder methods, it equips researchers and clinicians to design, validate, and deploy AI responsibly-and to keep learning as regulations evolve. Readers gain actionable tools, checklists, and frameworks to operationalize ethical AI, prepare for audits and certifications, and anticipate global convergence (“Brussels Effect”) shaping digital health. The volume serves clinicians, administrators, developers, policy and legal experts seeking rigorous yet accessible guidance.Maps global health-AI regulation (EU AI Act, GDPR, HIPAA, WHO) into clear organizational requirements and governance modelsDemonstrates compliance in practice through hospital and public-health case studies, checklists, and implementation guidanceOperationalizes ethics-to-action via explainability, bias mitigation, and privacy-preserving methods (e.g., federated learning, differential privacy)Demonstrates compliance in practice through hospital and public-health case studies, checklists, and implementation guidance