Explainable AI in Clinical Practice: Advanced Applications and Future Directions - Brossura

Brossura

ISBN 10: 0443453748 ISBN 13: 9780443453748

Casa editrice: Academic Press, 2026

Riassunto

Explainable AI in Clinical Practice: Advanced Applications and Future Directions builds on foundational concepts to explore the practical implementation and emerging trends of transparent AI in healthcare. Featuring contributions from leading experts, this volume presents advanced methodologies, real-world case studies across various medical specialties, and strategies for overcoming ethical, regulatory, and operational challenges. The book offers comprehensive frameworks for integrating explainable AI into clinical workflows, emphasizing trust, patient understanding, and regulatory compliance. In addition, it examines future technologies such as federated learning, multimodal systems, and human-AI collaboration, providing insights into the evolving landscape of AI in medicine. Essential for healthcare professionals, researchers, and policymakers, this volume aims to accelerate the responsible adoption of explainable AI, ultimately enhancing patient care, clinical decision-making, and healthcare system efficiency.

Su questo libro

Informazioni sugli autori

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.

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. 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.

Dalla quarta di copertina

Explainable AI in Clinical Practice: Advanced Applications and Future Directions builds on foundational concepts to explore the practical implementation and emerging trends of transparent AI in healthcare. Featuring contributions from leading experts, this volume presents advanced methodologies, real-world case studies across various medical specialties, and strategies for overcoming ethical, regulatory, and operational challenges. It offers comprehensive frameworks for integrating explainable AI into clinical workflows, emphasizing trust, patient understanding, and regulatory compliance. The book also examines future technologies such as federated learning, multimodal systems, and human-AI collaboration, providing insights into the evolving landscape of AI in medicine. Essential for healthcare professionals, researchers, and policymakers, this volume aims to accelerate responsible adoption of explainable AI, ultimately enhancing patient care, clinical decision-making, and healthcare system efficiency.

Dettagli dell'edizione

• Editore: Academic Press
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 10: 0443453748
• ISBN 13: 9780443453748
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 376
• Redattore: Mallik
• Contatto del produttore: non disponibile
• Persona responsabile: LSWR Group; b.criscuolo@lswr.it; ; Via Spadolini, 7; Milano; Italia