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Artificial intelligence is transforming modern healthcare, but its widespread adoption depends on one critical factor "trust". This book explores how Explainable Artificial Intelligence (XAI) enhances transparency, interpretability, and accountability in AI-driven healthcare systems, enabling clinicians, patients, researchers, and policymakers to make informed and confident decisions. Covering theoretical foundations, state-of-the-art methodologies, ethical and regulatory considerations, and real-world healthcare applications, the book provides a comprehensive roadmap for designing and deploying trustworthy AI solutions.
Dr. Achin Jain: Distinguished researcher and academician with 13+ years of experience specializing in AI applications in healthcare. PhD from Guru Gobind Singh Indraprastha University focusing on
feature selection methods for sentiment classification. Expertise in Machine Learning, Deep Learning, and Medical Image Analysis. Published 23 SCI/SCOPUS/ESCI-indexed journal articles, 10 conference papers, and 2 book chapters. Actively mentors graduate students and leads interdisciplinary research initiatives.
Dr. Shalini Gambhir: Distinguished academician and researcher in Computer Science and Engineering. Currently Programme Coordinator for B.Tech CSE at VIPS. Expertise in machine learning, deep learning, AI, blockchain, and cloud computing. Multiple SCI, Scopus and Web of Science-indexed publications. Active reviewer for international conferences and journals. Successfully mentored student teams in high-impact research and competitions.
Dr. Saurav Mallik: Research Scientist at University of Arizona with extensive postdoctoral experience at Harvard T.H. Chan School of Public Health (2019-2022) and University of Texas Health Science Center (2018-2019). PhD in Computer Science & Engineering from Jadavpur University. Recipient of "Emerging Researcher in Bioinformatics" award (2020) and "Young Scientist Award" (2021). Coauthored 230+ research papers with 3000+ citations (h-index=30). Editor for multiple journals including Frontiers in Genetics and BMC Bioinformatics. Research interests include machine learning, deep learning, computational biology, data mining.
Dr. Arvind Panwar: Researcher with 15+ years of experience in Computer Science and Engineering. PhD from Guru Gobind Singh Indraprastha University on secure cloud-based blockchain framework for health record management. Expertise in blockchain, information security, and data analytics. Published 9 SCI/SCOPUS-indexed journal articles, 15 conference papers, and 18 book chapters. Currently editing three books for prestigious publishers. Holds 8 granted patents and 11 published patents in blockchain, AI, and IoT applications.
Prof. (Dr.) Preecha Yupapin: Dr. Preecha Yupapin received the Ph.D. degree in electrical engineering from the City, University of London, UK in 1993. He is currently the full Professor in the Department of Electrical Technology, School of Industrial Technology, Institute of Vocational Education Northeastern Region 2, Sakonnakhon, Thailand. His current research interests are relativistic electronics; plasmonic and microstrip circuits; quantum technologies; deep and machine learning.