Intelligent Precision Healthcare

Multimodal Generative AI for Neurodegenerative Disease Management
1. Auflage 2026
ISBN: 978-1-041-12867-0
Verlag: Taylor & Francis Ltd

Buch, Englisch, 336 Seiten, Format (B × H): 178 mm x 254 mm

Artificial Intelligence (AI) is reshaping modern healthcare in remarkable ways. This book explores how multimodal generative AI integrates clinical, genetic, and imaging data to improve the understanding and management of neurodegenerative diseases. Bridging technology and medicine, it offers accessible insights into next-generation precision healthcare and its real-world impact. Designed for researchers, clinicians, and informed readers alike, it highlights practical applications, ethical considerations, and future possibilities in AI-driven neurological care.

This volume provides a comprehensive examination of how multimodal generative AI techniques can be applied to the diagnosis, monitoring, and management of neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases. It covers foundational concepts in precision healthcare, multimodal data integration (including genetic, clinical, and neuroimaging data), and advanced modeling approaches for disease progression prediction. The book also explores synthetic data generation for rare conditions, explainable and interpretable AI models for clinical decision support, and AI-driven early detection systems. Dedicated chapters address ethical, legal, and regulatory considerations, interdisciplinary collaboration, and patient-centric AI design. Through case studies and real-world applications, the book demonstrates how intelligent systems can enhance personalized treatment planning, improve clinical outcomes, and support data-driven healthcare innovation.

Zielgruppe

Academic, Postgraduate, and Professional Practice & Development

Autoren/Hrsg.

Koundal, Deepika

Khan, Mudassir

Atul Mahajan, Rupali

Dey, Rajesh

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Neurologie, Klinische Neurowissenschaft
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Bildgebende Verfahren, Nuklearmedizin, Strahlentherapie Radiologie, Bildgebende Verfahren
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Gesundheitswirtschaft
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Naturwissenschaften Biowissenschaften Biowissenschaften Genetik und Genomik (nichtmedizinisch)
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

1. Decoding Psychiatric Disorders: Multimodal Brain Imaging Powered by Machine Learning and Deep Learning. 2. Generative AI-Driven Framework for Managing Neurodegenerative Disorders: Focus on Alzheimer’s Detection. 3. Convolutional Pathways to Neurodegeneration: An Image-Based Predictive Model for Alzheimer’s. 4. Generative AI for Early Diagnosis of Neurodegenerative Diseases: A Study on Multimodal Fusion Framework. 5. Introduction to Neurodegenerative Diseases. 6. Personalized Treatment Planning Through AI Insights. 7. AI-Powered Early Detection and Monitoring of Neurodegenerative Diseases. 8. Temporal Diffusion–Guided Assessment for Optimized Clinical Categorization of Alzheimer’s Disease Using Multimodal Neural Features. 9. Transforming Healthcare with Generative AI: Opportunities, Risks, and Ethical Considerations. 10. AI-Driven Early Detection of Multiple Sclerosis Through Explainable Deep Learning. 11. Principles of Precision Healthcare Toward Pancreatic Cancer. 12. Synthetic Data Generation for Emphysema Disease Research. 13. AI-Driven Early Detection of Neurodegenerative Disorders. 14. Intelligent Multimodal Imaging Framework for Robust and Real-Time Brain Tumor Detection. 15. AI-Powered Multimodal Approaches for Early Detection and Personalized Management of Neurodegenerative Diseases. 16. Multimodal Classification of Meningitis Etiologies From CSF Profiles Using a CNN-BiGRU Hybrid Model Optimized with the Sparrow Search Algorithm. 17. Deep Learning Approaches for Autism Spectrum Disorder Detection: Performance Analysis and a Hybrid ViT–GNN Model. 18. Multimodal Fusion Attention Graph Neural Networks for Alzheimer’s Dementia Detection. 19. Advancements in Magnetic Resonance Imaging Aimed at Alzheimer’s Assessment, Lung Cancer Identification, Characterization, and Staging: An In-Depth Analysis. 20. Integrating NLP and EHR for Next-Generation Mental Health Analytics. 21. A Multistage Deep Learning Approach for Intracranial Aneurysm Detection.

Über Autor(innen)

Deepika Koundal is a distinguished academic at the University of Eastern Finland, specializing in Artificial Intelligence, Machine Learning, and healthcare analytics. Her research focuses on intelligent data-driven solutions for medical and biomedical applications, contributing significantly to interdisciplinary innovation and global research collaborations. She has published extensively in reputed international journals and actively leads research initiatives in AI-driven healthcare technologies, fostering academic excellence and industry partnerships worldwide.

Mudassir Khan is an accomplished academician and researcher specializing in Big Data Analytics, Deep Learning, AI, and healthcare applications. Holding a Ph.D. in Big Data Analytics using Deep Learning, he is currently pursuing a postdoctoral fellowship at Multimedia University, Malaysia, while serving as Assistant Professor at King Khalid University, Saudi Arabia. With over 14 years of experience, he has authored 100+ SCI/SCOPUS-indexed papers, four books, and edited numerous volumes with leading publishers.

Rupali A. Mahajan is an accomplished academician and researcher specializing in Artificial Intelligence, Machine Learning, Deep Learning, and Data Science applications. Holding a Ph.D. in Computer Science and Engineering, she completed her postdoctoral research at the Singapore Institute of Technology. She currently serves as Associate Professor in the Department of Data Science at Vishwakarma Institute of Information Technology, Pune. With over 17 years of professional experience, she has authored 7 books, published 50+ research papers, and held 18 patents across diverse domains.

Rajesh Dey is a distinguished researcher specializing in Artificial Intelligence, Machine Learning, and FinTech. Currently a Postdoctoral Fellow at IIUM, Malaysia, he holds a Ph.D. in Adaptive Signal Correction. With over 17 years of experience, he has published extensively and contributed to embedded systems, IoT, robotics, and sustainable EV technologies. He has served in key academic and industry roles, including Associate Professor and Technical Director. A certified Design Thinking Coach, he actively collaborates with international universities and research institutions, mentoring students and driving innovation in emerging technologies.