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.
Table of Contents
1. Ensuring Trust in Healthcare Robotics: The Essential Role of Explainable AI2. XAI implementation in traditional alternate medicine system
3. Explainable Computational Intelligence in Bio and Clinical Medicine
4. Enhancing Medical AI Interpretability Using Heatmap Visualization Techniques
5. An interpretation-model-guided classification method for malignant pulmonary nodule
6. Case Studies: Explainable AI for Healthcare 5.0
7. OML-GANs: An Optimized Multi-Level Generative Adversarial Networks Model for Multi-Omics Cancer Subtype Classification
8. Explainable Artificial Intelligence in Epilepsy Management: Unveiling the Model Interpretability
9. Revolutionizing Cancer Diagnosis with AI-Enhanced Histopathology and Deep Learning: A Study on Enhanced Image Analysis and Model Explainability
10. Unveiling Explainable Artificial Intelligence (XAI) in Advancing Precision Medicine: An Overview
11. Pneumonia and Brain Tumors Diagnosis Using Machine Learning Algorithms
12. Explainable Artificial Intelligence in Medical Research: A Synopsis for Clinical Practitioners Comprehensive XAI Methodologies
13. Advancing Explainable AI and Deep Learning in Medical Imaging for Precision Medicine and Ethical Healthcare
14. Leveraging Explainable AI in Deep Learning for Brain Tumor Detection
15. Unveiling the Root Causes of Diabetes Using Explainable AI
16. Explainable AI for Melanoma Diagnosis through Dermosopic Images: Recent Findings and Future Directions
17. Enhancing Multi-Omics Cancer Subtype Classification Using Explainable Convolutional Neural Networks
18. Explainable Convolutional Neural Network for Parkinson’s Disease Detection
19. Data analytics and cognitive computing for digital health: A Generic Approach and a review of emerging technologies, challenges, and research directions
20. New challenges and opportunities to explainable artificial intelligence (XAI) in smart healthcare
Authors
Tanzila Saba Research Professor and Associate Chair, Information Systems Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh, KSA. 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 Taher Azar Research Associate Professor, Prince Sultan University, Riyadh, Kingdom Saudi ArabiaAssociate Professor, Faculty of Computers and Artificial intelligence, Benha University, Egypt. 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 Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon; Department of Applied Data Science, Noroff University College, Kristiansand, Norway.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.
