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Taschenbuch. Zustand: Neu. Explainable Artificial Intelligence in the Digital Sustainability Administration | Proceedings of the 2nd International Conference on Explainable Artificial Intelligence in the Digital Sustainability Administration (AIRDS 2024) | Alhamzah Alnoor (u. a.) | Taschenbuch | Lecture Notes in Networks and Systems | xiii | Englisch | 2024 | Springer | EAN 9783031637162 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer Nature Switzerland, Springer Nature Switzerland, 2024
ISBN 10: 303163716X ISBN 13: 9783031637162
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores current research trends in the context of the explainable artificial intelligence's impact on the digital sustainability trend while delving into case studies on education, tourism, marketing, and finance. These trends are examined through various case studies utilizing distinct analytical methods. The chapters are expected to support scholars and postgraduate students in furthering their research in this field and in recognizing prospective advancements in the applications of artificial intelligence.
Sprache: Englisch
Verlag: Springer-Nature New York Inc, 2024
ISBN 10: 303163716X ISBN 13: 9783031637162
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In den WarenkorbPaperback. Zustand: Brand New. 403 pages. 9.25x6.10x9.21 inches. In Stock.
Sprache: Englisch
Verlag: Elsevier Science Publishing Co Inc Mai 2025, 2025
ISBN 10: 0443239797 ISBN 13: 9780443239793
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - In an era where Artificial Intelligence (AI) is revolutionizing healthcare, Explainable AI in Healthcare Imaging for Precision Medicine addresses the critical need for transparency, trust, and accountability in AI-driven medical technologies. As AI becomes an integral part of clinical decision-making, especially in imaging and precision medicine, the question of how AI reaches its conclusions grows increasingly significant. This book explores how Explainable AI (XAI) is transforming healthcare by making AI systems more interpretable, reliable, and transparent, empowering clinicians and enhancing patient outcomes.Through a comprehensive examination of the latest research, real-world case studies, and expert insights, this book delves into the application of XAI in medical imaging, disease diagnosis, treatment planning, and personalized care. It discusses the technical methodologies behind XAI, the challenges and opportunities of its integration into healthcare, and the ethical and regulatory considerations that will shape the future of AI-assisted medical decisions.Key areas of focus include the role of XAI in improving diagnostic accuracy in fields such as radiology, pathology, and genomics and its potential to enhance collaboration between AI systems, healthcare professionals, and patients. The book also highlights practical applications of XAI in personalized medicine, showing how explainable models help tailor treatments to individual patients, and discusses how XAI can contribute to reducing bias and improving fairness in medical decision-making.Written by leading experts in AI, healthcare, and precision medicine, Explain[S3G1] able AI in Healthcare Imaging for Precision Medicine is an essential resource for researchers, clinicians, students, and policymakers. Whether you are looking to stay at the forefront of AI innovations in healthcare or seeking to understand how explainability can build trust in AI systems, this book provides the insights and knowledge needed to navigate the evolving landscape of AI in medicine. It invites readers to explore how XAI can revolutionize healthcare and precision medicine, shaping a future where AI is both powerful and trustworthy.