Explainable AI in Clinical Practice: Methods, Applications, and Implementation bridges the gap between artificial intelligence capabilities and their practical implementation in healthcare. The book explores applications of explainable AI in diagnostic support and treatment planning, offering insights into making AI systems interpretable and accountable. Through real-world case studies and ethical frameworks, readers learn to transform opaque AI systems into tools that enhance clinical practice while maintaining high patient care standards. This volume unites leading experts to provide a comprehensive framework for implementing explainable AI, ensuring that AI-driven decisions are transparent, trustworthy, and clinically sound. Targeted solutions in the book cater to diverse stakeholders in the healthcare AI ecosystem. Healthcare professionals will gain confidence in integrating AI tools, while technical teams will receive implementation guidelines. This book is essential for anyone seeking to responsibly and effectively navigate the complexities of AI in healthcare.
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Dr. Arvind Panwar is a distinguished researcher and academician with over 15 years of experience in Computer Science and Engineering. He holds a Ph.D. from Guru Gobind Singh Indraprastha University, focusing on a secure cloud-based blockchain framework for health record management. His expertise includes blockchain technology, information security, cybersecurity, and data analytics.
Dr. Panwar has authored 9 SCI/SCOPUS-indexed journal articles, 15 conference papers, and 18 book chapters. He is currently editing three significant books: Data Analytics and Artificial Intelligence for Predictive Maintenance in Industry 4.0, Qubits Unveiled: Quantum Computing Solutions for Efficient Supply Logistics, and Energy Efficient Internet of Things-Based Wireless Sensor Networks. A prolific innovator, he holds 8 granted patents and 11 published patents related to blockchain, AI, and IoT applications. His contributions to mentoring graduate students and engaging in global collaborations, including a visiting professorship in Kazakhstan, further establish him as a leading figure in bridging research and industry.
Dr. Achin Jain is a distinguished researcher and academician with over 13 years of experience, specializing in Artificial Intelligence applications in healthcare. He holds a Ph.D. from Guru Gobind Singh Indraprastha University, where his research focused on designing feature selection methods for sentiment classification using Computational Intelligence Techniques. Dr. Jain’s expertise encompasses Machine Learning, Deep Learning, and advanced methodologies for Medical Image Analysis and AI-driven Disease Diagnosis. A prolific scholar, Dr. Jain has published 23 SCI/SCOPUS/ESCI- indexed journal articles, 10 conference papers, and 2 book chapters, with a strong emphasis on AI’s transformative role in medical diagnostics. He actively mentors graduate students, leads interdisciplinary research initiatives, and fosters international collaborations to advance AI innovations in healthcare. Dr. Jain’s contributions in merging technological advancements with medical applications highlight his dedication to leveraging AI for improving patient care, making him a leading voice in the field of AI-driven medical research.
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