Generative AI in Neurodegenerative Disorders

Innovations, Views, and Obstacles
1. Auflage 2025
ISBN: 978-87-438-0175-7
Verlag: River Publishers

Buch, Englisch, 450 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: River Publishers Series in Biomedical Engineering

This book delves into the transformative power of AI in the realm of neurodegenerative diseases,
covering topics such as ALS, Huntington’s, Parkinson’s, and Alzheimer’s. Generative AI
provides new opportunities for early diagnosis, precise therapy, and individualized rehabilitation,
which are crucial as these conditions remain major obstacles for healthcare providers
and researchers.

Researchers, physicians, AI developers, and healthcare professionals will find this book
an invaluable resource for understanding how AI is influencing the development of treatments
for neurodegenerative diseases. It describes important obstacles and future directions while
providing insights into the newest breakthroughs, thus bridging the gap between technology
and practical clinical applications. Anyone involved in neurodegenerative healthcare, from
scientists conducting AI-driven medical research to physicians seeking to incorporate AI into
patient care or AI professionals investigating new healthcare applications, will find the information
and insights they need in this comprehensive book.

Predictive analytics, biomarker identification, and drug discovery are being transformed
by AI-driven models, such as deep neural networks, generative adversarial networks (GANs),
and variational autoencoders (VAEs). This book offers a comprehensive examination of these
developments. Robots, wearable sensors, and cognitive therapy platforms are some of the
AI-enhanced rehabilitation tools covered, as are AI-integrated cutting-edge technologies like
fMRI and MRI, gene-editing methods like CRISPR, and more.

In addition to discussing recent technical developments, this book takes a close look at
the data privacy, ethics, and regulatory issues that arise when using AI to study neurodegenerative
disorders. Issues like algorithmic bias, model explainability, and fair AI-driven healthcare
are thoroughly investigated in light of the growing usage of AI models in clinical decision-making,
mental health applications, and cognitive rehabilitation.

Zielgruppe

Academic, Postgraduate, and Professional Practice & Development

Autoren/Hrsg.

Nag, Anindya

Karim, Asif

Hassan, Md. Mehedi

Reddy C, Kishor Kumar

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Neurologie, Klinische Neurowissenschaft
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Neurowissenschaften, Kognitionswissenschaft
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik

Inhaltsverzeichnis

1. Generative AI: A New Frontier in Understanding and Treating Neurodegenerative Diseases. 2. Generative AI-enhanced Diagnostic Systems: Revolutionizing Early Disease Detection through Advanced Predictive Analytics. 3. Obstacles and Opportunities: Generative AI in the Context of Neurodegenerative Disorders. 4. Generative AI in the Evolution of Gene Therapy: A Paradigm Shift in Genetic Engineering. 5. A Generative Predictive Model for Medical Data via PCA and Iterative K-means Fusion. 6. Exploring the Promises and Perils of Implementing Generative AI into Mental Health Care and Emotional Well-being Support of the General Public: A Comprehensive Overview. 7. Navigating Autism Spectrum Disorder: A Fusion of Deep Learning and Explainable AI for Enhanced Detection and Classification. 8. Generative AI-augmented Mental Health Support: The Impact of Generative Models on Therapeutic Practice. 9. AI and Neurodegenerative Disorders: From Early Diagnosis to Advanced Care. 10. Prediction of Alzheimer’s and Parkinson’s Diseases: AI Perspectives. 11. Advanced Fingerprint Authentication System and Neurodegenerative Disorder Multi-modal Pattern Recognition Techniques using Deep Learning. 12. Convolutional Neural Network Based Biomarkers for Alzheimer’s Diagnosis and Prognosis. 13. Generative AI Novel Drug Discovery Avenues. 14. AI-driven Innovations: Revolutionizing the Management of Neurodegenerative Disorders. 15. Generative AI for Enhancing Cognitive Rehabilitation Patients with Neurodegenerative Disorders.

Über Autor(innen)

Anindya Nag obtained an M.Sc. in Computer Science and Engineering from Khulna University in Khulna, Bangladesh, and a B.Tech. in Computer Science and Engineering from Adamas University in Kolkata, India. He is currently a Lecturer in the Department of Computer Science and Engineering at the Northern University of Business and Technology in Khulna, Bangladesh. His research focuses on health informatics, medical Internet of Things, neuroscience, and machine learning. He serves as a reviewer for numerous prestigious journals and international conferences. He has authored and co-authored about 42 publications, including journal articles, conference papers, and book chapters, and has co-edited five books.

Md. Mehedi Hassan is a dedicated and accomplished researcher. He completed his Master of Science degree in computer science and engineering at Khulna University, Khulna, Bangladesh in 2024 and completed his B.Sc. degree in Computer Science and Engineering from North Western University, Khulna in 2022, where he excelled in his studies and demonstrated a strong aptitude for research. As the founder and CEO of The Virtual BD IT Firm and VRD Research Laboratory, Bangladesh, Mehedi has established himself as a highly respected leader in the fields of biomedical engineering, data science, and expert systems. He is a member of the prestigious IEE. Mehedi’s research interests are broad and include important human diseases, such as oncology, cancer, and hepatitis, as well as human behavior analysis and mental health. He is highly skilled in association rule mining, predictive analysis, machine learning, and data analysis, with a particular focus on the biomedical sciences. Mehedi has published 74 articles in various international top journals and conferences and has published a book titled, ""Federated Deep Learning for Healthcare: A Practical Guide with Challenges and Opportunities"". His work has been well-received by the research community and has significantly contributed to the advancement of knowledge in his field. Additionally, he serves as an academic editor and a reviewer for 56 prestigious journals.

Dr. Asif Karim is a Research Active Lecturer at Charles Darwin University, Australia. His research interests include applying machine intelligence to fields such as smart contracts and health informatics. Besides being an active researcher, He has considerable industry experience in IT, primarily in software engineering. In addition, He is also involved in active teaching to undergraduate and postgraduate students in a range of different computer science related courses.

Dr. C Kishor Kumar Reddy is currently working as Associate Professor, Dept. of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, India and has research and teaching experience of more than 10 years. He has published more than 50 research papers in national and international conferences, book chapters, and journals indexed by Scopus and others. He is an author of two text books and has co-edited seven books. He acted as the special session chair for Springer FICTA 2020, 2022, SCI 2021, INDIA 2022 and IEEE ICCSEA 2020 conferences. He is the corresponding editor of AMSE 2021 conferences, published by IoP Science JPCS. He is the member of ISTE, CSI, IAENG, UACEE, IACSIT.