Smart Diagnostics and Data-Driven Healthcare AI Approaches and the Role of Cybersecurity
Description
This book presents an accessible overview of how artificial intelligence (AI) is being used to transform diagnosis and treatment, while also highlighting the importance of building secure and trustworthy healthcare systems. In addition, the contributions explore the transformative impact of AI on medical diagnostics, clinical decision-making, and predictive analytics. With growing advancements in machine learning, deep learning, and clinical data analytics, smart diagnostics are becoming increasingly accurate, timely, and personalized. However, as AI systems rely heavily on sensitive patient data, concerns regarding data privacy, cybersecurity, and ethical usage are rising in parallel. These AI-powered tools support clinical decision-making, help reduce human error, and can lead to more personalized and effective treatment plans. However, to function effectively, these intelligent systems need access to massive amounts of sensitive health information, which is why cybersecurity becomes critical. This book brings together experts in AI, medicine, and cybersecurity to offer insights into how cutting-edge technologies can safely and ethically improve patientcare. Current challenges in AI deployment in healthcare settings, including patient data protection, system vulnerabilities, and compliance with global data privacy regulations (e.g., HIPAA, GDPR) are addressed. Bridging medicine, technology, and digital ethics, this book offers valuable insights for researchers, clinicians, data scientists, and healthcare policymakers striving to build intelligent, secure, and future-ready healthcare systems. This book also aims to foster a deeper understanding of how intelligent, secure, and responsible AI applications can drive the future of healthcare delivery worldwide.>
Emphasizes smart diagnostics and clinical decision support systems, which are at the forefront of medical innovation Includes case studies and explores deployment challenges, cybersecurity incidents, and operational best practices Incorporates ethical/regulatory perspectives, privacy laws, bias mitigation, and societal impact of AI in healthcare
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Persons
Suman Kumar Swarnkar, Ph.D., is an Assistant Professor in the Department of Computer Science and Engineering at Shri Shankaracharya Institute of Professional Management and Technology in Raipur, India. He received his Ph.D. (CSE) from Kalinga University, Nayaraipur and his M.Tech. (CSE) from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. He has contributed to book chapters and is a member of IEEE, IAENG, ASR, IFERP, ICSES, Internet Society, UACEE, IAOP, IAOIP, EAI, and CSTA. Dr. Swanrkar's research interests include intelligent data analysis, nature-inspired computing, machine learning, and soft computing.
Yogesh Kumar Rathore, Ph.D., is an Assistant Professor in the Department of Computer Science and Engineering at Shri Shankaracharya Institute of Professional Management and Technology in Raipur, India. He completed his Ph.D. in Information Technology from the National Institute of Technology, Raipur. Before that, he completed his M. Tech. from Chhattisgarh Swami Vivekanand Technical University and his B. Eng. from Pt. Ravishankar Shukla University. He has published two edited books, contributed chapters to internationally edited books, and authored a textbook on data mining. Dr. Rathore has also been involved in patenting with three Indian patents published and two USA-granted patents, showcasing his expertise in the field of computer science engineering.
Gunjan Chhabra, Ph.D., is an Associate Professor in the School of Science and Technology at Swami Rama Himalayan University in Dehradun, India. He has over 12 years of teaching and research experience, and his research interests primarily lie in artificial intelligence and image processing. He holds several patents in the computer science domain with nine patents granted and others under examination. Dr. Chhabra has authored books and edited volumes related to emerging technologies such as smart agriculture, IoT, and the Metaverse, highlighting their transformative potential for society.
Niraj Upadhayaya, Ph.D., is a Professor in the Department of Computer Science and Engineering at SRM University, Andhra Pradesh. He obtained his Ph.D. in Computer Engineering from University of the West of England, UK and served as Professor, Dean, and Principal at J.B. Institute of Engineering and Technology in Hyderabad, India for 17 years.
J. Somasekar, Ph.D., is a Professor and Head of the CSE (AI-Driven DevOps) program at Jain University in Bangalore, India. In 2024, he was appointed as a Research Fellow at INTI International University, Malaysia. He has delivered over 230 invited talks across India and internationally. Dr. Somasekar has published 50+ research papers, authored three books, and received multiple accolades including an All India GATE Rank of 43, the ANRF International Travel Grant, and the 'Innovative Collaborative Researcher' award. His research focuses on machine learning, image processing, data science, and cybersecurity.
Content
Introduction to Smart Diagnostics and Data-Driven Healthcare.- Artificial Intelligence in Clinical DecisionSupport Systems.- Predictive Modeling for Early Disease Detection.- Deep Learning in Medical Imaging and Diagnostics.- Natural Language Processing in Electronic Health Records.- CybersecurityChallenges in AI-Driven Healthcare Systems.- Privacy-Preserving Machine Learning in Healthcare.- Blockchain for Medical Data Security and Trust.- Explainable AI in Smart Diagnostics.- Ethical and LegalConsiderations.- Case Studies: Real-World Applications of Smart Diagnostics.- Future Directions in Secure and Intelligent Healthcare.