Explainable AI in Clinical Practice: Methods, Applications, and Implementation - Softcover

Softcover

ISBN 10: 0443441111 ISBN 13: 9780443441110

Verlag: Academic Press, 2026

Inhaltsangabe

Explainable AI in Clinical Practice: Methods, Applications, and Implementation bridges the gap between artificial intelligence capabilities and their practical implementation in healthcare. The book explores applications of explainable AI in diagnostic support and treatment planning, offering insights into making AI systems interpretable and accountable. Through real-world case studies and ethical frameworks, readers learn to transform opaque AI systems into tools that enhance clinical practice while maintaining high patient care standards. This volume unites leading experts to provide a comprehensive framework for implementing explainable AI, ensuring that AI-driven decisions are transparent, trustworthy, and clinically sound.

Targeted solutions in the book cater to diverse stakeholders in the healthcare AI ecosystem. Healthcare professionals will gain confidence in integrating AI tools, while technical teams will receive implementation guidelines. This book is essential for anyone seeking to responsibly and effectively navigate the complexities of AI in healthcare.

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Über die Autorinnen und Autoren

Arvind Panwar is a researcher and academic in the field of Computer Science and Engineering whose interests include blockchain technology, information security, cybersecurity, data analytics, and emerging digital technologies. His research focuses on the development of secure and scalable computing frameworks, including applications of blockchain in healthcare and data management. Dr. Panwar has contributed to scholarly research through journal articles, conference papers, book chapters, patents, and edited volumes. He is actively engaged in research, innovation, and academic collaboration, with work spanning blockchain, artificial intelligence, the Internet of Things, and cybersecurity. His activities include mentoring students, supporting interdisciplinary research initiatives, and participating in international academic collaborations. Through his research and educational contributions, he promotes the translation of advanced computing technologies into practical solutions for industry and society.

Dr. Achin Jain is a researcher and academic specializing in the application of artificial intelligence to healthcare. His research focuses on machine learning, deep learning, computational intelligence, medical image analysis, and AI-enabled approaches to disease diagnosis. He has contributed extensively to the scholarly literature through journal articles, conference publications, and book chapters addressing the use of AI in medical and healthcare applications.
Dr. Jain is actively involved in mentoring graduate students and leading interdisciplinary research activities that bring together expertise from computing and healthcare domains. He also promotes national and international research collaborations aimed at advancing innovative AI solutions for medical challenges. His work centers on developing and evaluating intelligent methods that support clinical decision-making and enhance healthcare outcomes through the responsible application of artificial intelligence.

Dr. Saurav Mallik is a Research Scientist in the Department of Pharmacology and Toxicology at The University of Arizona, USA. He previously served as a Postdoctoral Fellow at Harvard T.H. Chan School of Public Health (2019-2022) and held positions at the University of Texas Health Science Center at Houston (2018-2019) and the University of Miami Miller School of Medicine (2017-2018). Dr. Mallik earned his PhD in Computer Science and Engineering from Jadavpur University, India, in 2017, conducting research at the Indian Statistical Institute. He received a Research Associateship from CSIR, India, in 2017. With over 150 publications in high-impact journals, he has authored several books and patents. Dr. Mallik is an active member of IEEE, ACM, AACR, and Bioclues, and has collaborated with editors and reviewers for prestigious journals. His research focuses on Computational Biology, Bioinformatics, Bio-Statistics, and Machine Learning.

Dr. Aimin Li is an associate professor of Xi'an University of Technology, China. He got his master degree from Xi'an University of Technology, and doctoral degree from Xidian University. He previously worked as a visiting scientist in University of Texas Health Science Center, Houston, Texas, USA. His current research applications are in the areas of machine learning, bioinformatics, and regulatory networks. He has published 80+ research papers. He is also an editor of International Journal of Computational Biology and Drug Design, PC member of ICIBM (International Conference on Intelligent Biology and Medicine), and co-chair of BIBM IWRI 2020

Assoc. Prof. Dr. Korhan Cengiz is a senior researcher at the University of Hradec Králové, Czech Republic, and Associate Professor at Istinye University, Turkey. He holds a PhD in Electronics Engineering from Kadir Has University and has held academic roles in Turkey, the UAE, and Jordan. Dr. Cengiz has authored over 40 SCI/SCI-E articles, 10+ book chapters, 5 international patents, and edited more than 20 books. His research focuses on wireless sensor networks, IoT, signal processing, and 5G. He serves as Associate Editor for IEEE Transactions on Intelligent Transportation Systems, IEEE Potentials, and IET journals, and is a frequent keynote speaker at IEEE and Springer conferences. A Senior Member of IEEE and ACM, he has received multiple awards, including best paper and presentation honors at ICAT conferences.

Von der hinteren Coverseite

Explainable AI in Clinical Practice: Methods, Applications, and Implementation bridges the gap between artificial intelligence capabilities and their practical implementation in healthcare. As AI systems become prevalent in clinical decision-making, transparency and explainability are crucial. This volume unites leading experts to provide a comprehensive framework for implementing explainable AI, ensuring that AI-driven decisions are transparent, trustworthy, and clinically sound. The book explores applications of explainable AI in diagnostic support and treatment planning, offering insights into making AI systems interpretable and accountable. Through real-world case studies and ethical frameworks, readers learn to transform opaque AI systems into tools that enhance clinical practice while maintaining high patient care standards. Targeted solutions cater to diverse stakeholders in the healthcare AI ecosystem. Healthcare professionals gain confidence in integrating AI tools, while technical teams receive implementation guidelines. This book is essential for anyone seeking to navigate the complexities of AI in healthcare responsibly and effectively.

Details zur Ausgabe

• Verlag: Academic Press
• Erscheinungsdatum: 2026
• Sprache: Englisch
• ISBN 10: 0443441111
• ISBN 13: 9780443441110
• Einband: Taschenbuch
• Anzahl der Seiten: 526
• Herausgeber: Panwar
• Kontakt zum Hersteller: Elsevier Science & Technology; tradeorders@elsevier.com; ; London Wall 125; London; EC2Y 5AS; Vereinigtes Königreich
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