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

9780443441110: Explainable AI in Clinical Practice: Methods, Applications, and Implementation

Softcover

ISBN 10: 0443441111 ISBN 13: 9780443441110

Publisher: Academic Press, 2026

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.

Provides a comprehensive framework for implementing explainable AI in healthcare, ensuring that AI-driven decisions are transparent, trustworthy, and clinically sound
Includes real-world case studies that illustrate practical applications of explainable AI
Offers targeted solutions for diverse stakeholders in the healthcare AI ecosystem

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About the Authors

Dr. Arvind Panwar is a distinguished researcher and academician with over 15 years of experience in Computer Science and Engineering. He holds a Ph.D. from Guru Gobind Singh Indraprastha University, focusing on a secure cloud-based blockchain framework for health record management. His expertise includes blockchain technology, information security, cybersecurity, and data analytics.

Dr. Panwar has authored 9 SCI/SCOPUS-indexed journal articles, 15 conference papers, and 18 book chapters. He is currently editing three significant books: Data Analytics and Artificial Intelligence for Predictive Maintenance in Industry 4.0, Qubits Unveiled: Quantum Computing Solutions for Efficient Supply Logistics, and Energy Efficient Internet of Things-Based Wireless Sensor Networks. A prolific innovator, he holds 8 granted patents and 11 published patents related to blockchain, AI, and IoT applications. His contributions to mentoring graduate students and engaging in global collaborations, including a visiting professorship in Kazakhstan, further establish him as a leading figure in bridging research and industry.

Dr. Achin Jain is a distinguished researcher and academician with over 13 years of experience, specializing in Artificial Intelligence applications in healthcare. He holds a Ph.D. from Guru Gobind Singh Indraprastha University, where his research focused on designing feature selection methods for sentiment classification using Computational Intelligence Techniques. Dr. Jain’s expertise encompasses Machine Learning, Deep Learning, and advanced methodologies for Medical Image Analysis and AI-driven Disease Diagnosis. A prolific scholar, Dr. Jain has published 23 SCI/SCOPUS/ESCI- indexed journal articles, 10 conference papers, and 2 book chapters, with a strong emphasis on AI’s transformative role in medical diagnostics. He actively mentors graduate students, leads interdisciplinary research initiatives, and fosters international collaborations to advance AI innovations in healthcare. Dr. Jain’s contributions in merging technological advancements with medical applications highlight his dedication to leveraging AI for improving patient care, making him a leading voice in the field of AI-driven medical research.

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 Kr�lov�, 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.

From the Back Cover

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.

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