This book explores how artificial intelligence is revolutionizing the detection and treatment of diseases driven by oxidative stress. It presents cutting-edge approaches to biomarker analysis, predictive modeling, and AI-assisted diagnostics, offering a bridge between molecular biology and computational innovation.

The foundation of this work lies in decades of research on free radicals and oxidative stress, which play a central role in cancer, cardiovascular disease, neurodegeneration, and metabolic disorders. Traditional methods often fail to detect oxidative damage early or personalize therapies effectively. By integrating AI and machine learning, the book demonstrates how technology can overcome these limitations and advance precision medicine.

Organized into four sections, the book covers fundamentals of oxidative stress biology, AI/ML applications in disease detection, therapeutic targeting in specific disorders, and future trends in clinical implementation. Topics include biomarkers, antioxidants, deep learning in cancer mutation analysis, wearable biosensors, and explainable AI, supported by case studies and practical examples.

The volume is designed for biomedical researchers, clinicians, healthcare professionals, and AI/ML scientists, as well as graduate and doctoral students in biomedical sciences and computational biology. Industry professionals in pharmaceuticals and biotech will also find it a valuable resource for advancing personalized healthcare.

Dr. Swarup Roy is Professor of Computer Science & Engineering at Tezpur University, India, with specialization in Data Science and Computational Biology. A Fellow of the Institution of Engineers (FIE) and the Institution of Electronics and Telecommunication Engineers (FIETE), he has held senior academic positions at Sikkim University and NEHU Shillong. He earned his M.Tech. and Ph.D. from Tezpur University and completed postdoctoral fellowships at the University of Colorado, USA, and IIT Guwahati. Dr. Roy has published extensively in leading international journals and conferences and co-authored three books, including Fundamentals of Data Science: Theory & Practice and Biological Network Analysis (Elsevier, USA). He also serves as Associate Editor of IET Systems Biology, contributing to the advancement of computational approaches in biomedical sciences.

Dr. Debosree Ghosh is Assistant Professor of Physiology at Government General Degree College, Kharagpur II, West Bengal. A University Gold Medalist, she completed her Ph.D. in Physiology at the University of Calcutta in 2014. Her research spans oxidative stress, environmental physiology, reproductive health, neuroendocrine regulation, and AI-assisted personalized medicine. She has authored nearly 100 research papers and review articles in reputed journals such as Elsevier, Springer, Bentham, and Taylor & Francis, along with several book chapters on oxidative stress, antioxidants, phytomedicine, and artificial intelligence in healthcare. A reviewer for multiple international journals, she is a life member of PSI, ISCA, FIPS, SAAP, IUPS, and SBC. With an h-index of 20 and over 1,200 citations, Dr. Ghosh is recognized for advancing integrative and translational approaches in physiology and biomedical sciences.

Dr. Pankaj Kalita is Assistant Professor in Zoology at Eastern Karbi Anglong College, Assam University. He previously served in the PG Department of Biophysics at Pub Kamrup College under Gauhati University. Dr. Kalita earned his Ph.D. from Assam University after completing his M.Sc. at Dibrugarh University. His research interests include neuroscience, nanobiotechnology, and computational biology. He has published more than 25 research papers in journals of RSC, ACS, Elsevier, Springer, and Wiley, and contributed 16 book chapters. He has edited six research books, including Innovative Neurodiagnostics and Therapeutics (CRC Press, Taylor & Francis). Actively engaged in academic leadership, he has organized national seminars and currently leads two funded R&D projects, contributing to innovation in neurobiology and diagnostics.

Dr. Pietro Hiram Guzzi is full Professor of Information Processing Systems at Magna Graecia University of Catanzaro, Italy. Since 2008, he has been part of the Research Group in Computer Engineering, Bioinformatics, and Medical Informatics. His work focuses on bioinformatics and medical informatics, with major contributions in semantic modeling and graph-theoretic algorithms applied to biomedical data. As principal investigator, he has developed widely adopted algorithms and software environments for omics data analysis, including genomics, proteomics, and interactomics. He collaborates internationally with leading institutions such as Georgia Tech and Fondazione Bruno Kessler. His publications appear in top-tier computer science journals (IEEE Transactions on Computers, IEEE/ACM TCBB, ACM Computing Surveys) and prestigious biomedical journals (Proteomics, Cancer Research, British Journal of Haematology), underscoring his impact in computational biology and medical data science.