Explainable AI in Healthcare Imaging for Medical Diagnoses: Digital Revolution of AI

· ·
· Elsevier
Ebook
400
Pages
Eligible
This book will become available on March 21, 2025. You will not be charged until it is released.

About this ebook

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. - Provides step-by-step procedures to build a digital human model - Assists in validating predicted human motion using simulations and experiments - Offers formulation optimization features for dynamic human motion prediction

About the author

Tanzila Saba is a Research Professor and Associate Chair of the Information Systems Department in the College of Computer and Information Sciences, Prince Sultan University, Riyadh, KSA. Her primary research focus in recent years is medical imaging, pattern recognition, data mining, MRI analysis, and soft computing. She led more than fifteen research-funded projects. She has full command of various subjects and taught several courses at the graduate and postgraduate levels. She is Senior Member of IEEE. Dr. Tanzila is Leader of Artificial Intelligence & Data Analytics Research Lab at PSU and Active Professional Member of ACM, AIS, and IAENG organizations. She is PSU WiDS (Women in Data Science) Ambassador at Stanford University.

Prof. Ahmad Azar has received the M.Sc. degree in 2006 and Ph.D degree in 2009 from Faculty of Engineering, Cairo University, Egypt. He is a research associate Professor at Prince Sultan University, Riyadh, Kingdom Saudi Arabia. He is also an associate professor at the Faculty of Computers and Artificial intelligence, Benha University, Egypt. Prof. Azar is the Editor in Chief of International Journal of System Dynamics Applications (IJSDA) and International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) published by IGI Global, USA. Also, he is the Editor in Chief of International Journal of Intelligent Engineering Informatics (IJIEI), Inderscience Publishers, Olney, UK. Prof. Azar has worked as associate editor of IEEE Trans. Neural Networks and Learning Systems from 2013 to 2017. He is currently Associate Editor of ISA Transactios, Elsevier and IEEE systems journal. Dr. Ahmad Azar has worked in the areas of Control Theory & Applications, Process Control, Chaos Control and Synchronization, Nonlinear control, Renewable Energy, Computational Intelligence and has authored/coauthored over 200 research publications in peer-reviewed reputed journals, book chapters and conference proceedings. He is an editor of many books in the field of fuzzy logic systems, modeling techniques, control systems, computational intelligence, chaos modeling and machine learning. Dr. Ahmad Azar is closely associated with several international journals as a reviewer. He serves as international programme committee member in many international and peer-reviewed conferences. Dr. Ahmad Azar has been a senior member of IEEE since December 2013 due to his significant contributions to the profession. Dr. Ahmad Azar is the recipient of several awards including: Benha University Prize for Scientific Excellence (2015, 2016, 2017 and 2018), the paper citation award from Benha University (2015, 2016, 2017 and 2018). In June 2018, Prof. Azar was awarded the Egyptian State Prize in Engineering Sciences, the Academy of Scientific Research and Technology of Egypt, 2017. In July 2018 he was selected as a member of Energy and Electricity Research council, Academy of Scientific Research, Ministry of Higher Education. In August 2018 he was selected as senior member of International Rough Set Society (IRSS).

Prof. Seifedine Kadry’s research focuses on data science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET (Accreditation Board for Engineering and Technology) Program Evaluator for computing and engineering technology. He is a Fellow of IET, IETE, and IACSIT. He is a distinguished speaker for the IEEE Computer Society.

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