This book is a multidisciplinary guide to the integration of AI and ML in the diagnosis, treatment, and management of two of the most complex neurodegenerative disorders. It provides a deep dive into how such emerging technologies are reshaping clinical approaches and accelerating research breakthroughs in Alzheimer’s and Parkinson’s disease. 

The book opens with essential background on the pathological and clinical dimensions of these diseases, setting the stage for exploring how AI/ML tools are increasingly central to healthcare innovation. As neurodegenerative, conditions often evade early detection and precise treatment, this volume emphasizes the urgency of advanced computational solutions in bridging these gaps. 

Structured into five thematic sections, the book moves from foundational disease insights to AI/ML fundamentals in healthcare, followed by chapters on data-driven predictive modeling using clinical, imaging, and genetic datasets. It further explores AI-enabled diagnostics, personalized treatment planning, drug discovery, and wearable tech for real-time monitoring. The final section addresses ethical challenges and sustainability issues in AI implementation, enriched with global perspectives and case studies. 

Designed for researchers, clinicians, students, and industry professionals across neuroscience, biomedicine, AI, and healthcare engineering, this volume offers theoretical depth and practical direction for advancing neurodegenerative disease management through artificial intelligence.

Dr. Rakesh Sengupta is working as Assistant Professor, School of Interwoven Arts and Sciences, Krea University, Andhra Pradesh, India. He is a cognitive scientist specializing in vision, attention, and working memory. While working on his dissertation, Rakesh spent two years as a visiting fellow at Center for Mind/Brain Sciences (CiMec) at the University of Trento, Italy. He completed his PhD from the Center for Neural and Cognitive Science, University of Hyderabad in 2015 with a thesis entitled, "Computational and Empirical Investigations of Number, Time, and Memory". Upon completing his doctorate, Rakesh spent three years as a postdoc at Centre for Vision Research at York University in Toronto, Canada. His current research involves building computational models of the human number system and working memory using computational, theoretical, behavioral, and neuroimaging methods. He has published and has interest in the disciplines of Behavioral and Computational Neuroscience, Psychology, Philosophy of Mind, Neuroimaging, Biophysics, Quantum Computing, and Machine Learning. He is also a reviewer of journals Cognition and Nature Human Behavior.

Dr. Ganesh R. Naik is a leading expert in biomedical engineering and signal processing, ranked in the top 2% of researchers worldwide in Biomedical Engineering and signal processing (Stanford University Research). He received his Ph.D. in Electronics Engineering, specializing in biomedical engineering and signal processing, from RMIT University, Melbourne, Australia, in December 2009. Ganesh is a senior academic and research theme colead at Flinders University Sleep Research Institute. He held a Postdoctoral Research Fellow position at MARCS Institute, Western Sydney University (WSU), between July 2017 and July 2020. He led the data analysis team on a multimillion-dollar CRC project for sleep and developed several novel algorithms (wearables) related to sleep and cardiovascular projects.

Dr. Usha Desai received her PhD in Biomedical Signal Processing from REVA University, Bengaluru, and M. Tech and BE from Visvesvaraya Technological University, Belagavi, Karnataka. She received the DST International Travel Grant to present her research paper at 39th IEEE EMBS International Annual Conference held at South Korea. She has well expertized in working with physiological signals ECG, EEG, EMG. She has experience in working with Brain Computer Interface (BCI) project. Published her research articles and biomedical books in reputed journals and publishers. Her research interests include nonlinear signal processing, Characterization using transform domain techniques, and preprocessing of bio signals. She is Senior Member of IEEE and ISTE.