Explainable AI in Healthcare Imaging for Medical Diagnoses: Digital Revolution of Artificial Intelligence - Brossura

Brossura

ISBN 10: 0443239797 ISBN 13: 9780443239793

Casa editrice: Academic Press, 2025

Riassunto

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.

Su questo libro

Informazioni sugli autori

Prof. Tanzila Saba is a Research Professor and Associate Chair of the Information Sys tems 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 15 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.

Ahmad Azar is a Research Associate Professor at the Prince Sultan University, Riyadh, Kingdom Saudi Arabia. He is also an associate professor at the Faculty of Computers and Artificial intelligence, in Benha University, Egypt. He is the Editor in Chief of the International Journal of System Dynamics Applications (IJSDA), International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), and International Journal of Intelligent Engineering Informatics (IJIEI), among others. He is currently Associate Editor of ISA Transactions, Elsevier, and the IEEE systems journal. Dr. Azar works in the areas of control theory & applications, process control, chaos control and synchronization, nonlinear control, renewable energy, computational intelligence.

Seifedine Kadry is a Professor in the Department of Mathematics and Computer Science, at Norrof University College, in Norway. He has a Bachelor’s degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University. At present, his research focuses on data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguished speaker of IEEE Computer Society.

Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: Academic Press
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 0443239797
• ISBN 13: 9780443239793
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 572
• Redattore: Saba
• Contatto del produttore: Academic Press; https://shop.elsevier.com/; ; 230 PARK AVENUE, SUITE 800; NY; NY; 10169; U.S.A.
• Persona responsabile: non disponibile