About the Book
This book explores the transformative potential of Explainable AI (XAI) in enhancing healthcare delivery and XAI's role in fostering transparency, trust, and accountability in AI-driven medical decision-making. Covering technical foundations, practical applications, and ethical considerations, it offers valuable insights into how XAI can improve clinical decision-making, patient outcomes, and healthcare operations. Through real-world case studies, the book illustrates the practical benefits of XAI in diverse healthcare scenarios. It also addresses the challenges and solutions related to deploying XAI, making it an essential resource for professionals and researchers.
• Detailed exploration of the methodologies, algorithms, and regulatory considerations underpinning XAI in smart healthcare systems
• Diverse case studies demonstrating practical applications and benefits of XAI across various healthcare domains, enhancing understanding through tangible examples
• Exploration of innovative XAI applications in diagnosis, treatment, patient monitoring, and care delivery, showcasing its potential to revolutionize healthcare practices and improve outcomes
• Discussion on how XAI promotes patient engagement by providing clear explanations of AI-driven diagnoses or treatment plans, enhancing patient understanding and participation in their healthcare
• Breakdown of XAI techniques, algorithms, and interpretability strategies, helping medical professionals understand and trust AI-driven decision-making processes
Table of Contents:
1 Introduction to Explainable AI in Smart Healthcare Systems 2. Understanding the Framework of Explainable AI in Healthcare 3. The Role of Transparency and Interpretability in Healthcare AI 4. Ethical Considerations in Implementing Explainable AI in Healthcare 5. Advancing Healthcare with Explainable AI: Enhancing Patient Monitoring and Outcomes 6. Navigating Interpretability in AI: Balancing Performance, Standards, and Practical Guidance 7. Applications of Explainable AI in Diagnosis and Treatment 8. Challenges and Solutions in Deploying Explainable AI in Smart Healthcare Systems 9. Case Studies: Real-world Examples of Explainable AI in Healthcare 10. Future Directions and Innovations in Explainable AI for Healthcare
Appendix -A
About the Author :
Aman Kataria, PhD, is a Faculty in the domain of Electronics and Communication at the University Centre for Research and Development, Chandigarh University, Gharuan, Mohali, Punjab, India. He worked previously as an Assistant Professor at Amity Institute of Defence Technology, Amity University, Noida, Uttar Pradesh, India. He received the B.Tech. degree in Electronics and Communication engineering from the state government engineering institute, Malout Institute of Management and Information Technology (MIMIT), Malout, in 2010, and the Master’s and PhD degrees in Electronics and Instrumentation Control Engineering from the Thapar Institute of Engineering and Technology, Patiala, Punjab, India in 2013 and 2020, respectively. He worked as a Lecturer at the Electronics and Communication Department, National Institute of Technology, Hamirpur. He worked as a Project Associate with the CSIR-Central Scientific Instruments Organization from August 2020 to December 2022. At CSIO, he worked on Texas Instruments DLP5530Q1EVM, in which he had the experience of projecting customized images on the screen of DMD which assisted in the projection of vital information on the screen of Head-up Display (HUD), to be deployed in fighter aircraft of Indian Air force. He has contributed to various research activities while publishing papers in the various SCIE and Scopus-indexed journals and conference proceedings. He has published four international patents also. His research interests include machine learning, artificial intelligence, image processing, cyber-physical systems, the Internet of Things, and soft computing.
Sita Rani, PhD, works in the Department of Computer Science and Engineering at Guru Nanak Dev Engineering College, Ludhiana. She earned her Ph.D. in Computer Science and Engineering from I.K. Gujral Punjab Technical University, Kapurthala, Punjab in 2018. She completed Postdoc from Big Data Mining and Machine Learning Lab, South Ural State University, Russia from May, 2022 to August, 2023. She has also completed Post Graduate Certificate Program in Data Science and Machine Learning from Indian Institute of Technology, Roorkee in 2023. She has more than 20 years of academic, administration, and research experience. She is an active member of ISTE, IEEE and IAEngg. She has been recognized in the Top 2% Scientists list published by Stanford University for the year 2024. She is the receiver of ISTE Section Best Teacher Award- 2020, and International Young Scientist Award-2021. She has contributed to the various research activities while publishing articles in the renowned SCI and Scopus journals and conference proceedings. She has published several national and international patents and authored, edited and coedited 11 books. Dr. Rani has delivered many expert talks in A.I.C.T.E. sponsored Faculty Development Programs and key note talks in many National and International Conferences. She has also organized many International Conferences during her 20 years of experience. She is the member of Editorial Board and reviewer of many international journals of repute. She has also served as vice-president of SME and MSME (UT Council), Women Indian Chamber of Commerce and Industry (WICCI) Since November, 2021 to February, 2025. Her research interest includes, Data Science, Artificial Intelligence, Machine Learning, Blockchain Technology, Smart Healthcare, and Sustainable Development.
