Multimodal Data Fusion in Healthcare

AI Approaches for Precision Diagnosis

Akansha Singh, Anuradha Dhull, Monika Lamba, Krishna Kant Singh

• Publisher: Academic Press
• Year: 2026
• Topic: Biological Sciences
• eBook ISBN:
  9780443440267
• Subtopic: Biology
• Index: Biological Sciences

Multimodal Data Fusion in Healthcare: AI Approaches for Precision Diagnosis explores the transformative potential of AI in modern medicine by integrating diverse data sources such as medical imaging, genomics, EHRs, and wearable sensors. It highlights how AI technologies are revolutionizing healthcare systems through personalized and proactive diagnostics. The book covers cutting-edge methodologies, real-world applications, and the challenges of multimodal data fusion. Topics include AI-driven diagnostics, precision medicine, real-time patient monitoring, and the integration of clinical, genomic, and wearable data, providing both theoretical foundations and practical insights. This book is essential for healthcare professionals, data scientists, and engineers, offering clear frameworks for integrating diverse data types. It addresses crucial issues like data interoperability, privacy, and technical constraints, providing practical solutions. It serves as an invaluable reference for understanding and applying AI advancements in diagnostic precision and personalized medicine. - Explores cutting-edge AI methodologies and their real-world applications in healthcare diagnostics - Provides comprehensive frameworks for integrating multimodal data, including medical imaging, genomics, EHRs, and wearable sensors - Addresses critical issues such as data interoperability, privacy, and technical constraints, offering practical solutions for academic and clinical settings

Table of contents

• Title of Book
• Chapter 1 Introduction to multimodal data fusion in healthcare
• Chapter 2 DeepSeek and multimodal AI in healthcare: enabling precision diagnosis and smart clinical decision support
• Chapter 3 From data to diagnosis: enhancing medical predictions with explainable AI
• Chapter 4 Multimodal data fusion healthcare applications with Internet of Things
• Chapter 5 Integration of natural language processing with electronic health records
• Chapter 6 The neurological weather forecast: predicting cognitive storms before they arise
• Chapter 7 AI–driven models for early detection and prevention of cardiovascular diseases
• Chapter 8 Wearable sensor and clinical data fusion for cardiovascular risk prediction
• Chapter 9 ACUCARE—an analysis of multiple disease prediction system
• Chapter 10 MobileSwin-GI: a state-of-the-art hybrid deep learning model for gastrointestinal disease diagnosis
• Chapter 11 Accelerating rare disease detection using generative artificial intelligence, federated learning, and multimodal data integration
• Chapter 12 Enhancing brain tumor diagnosis using multimodal magnetic resonance imaging and computed tomography imaging with machine learning
• Chapter 13 Artificial intelligence–driven multimodal fusion for early detection of neurodegenerative diseases
• Chapter 14 Multimodal data fusion with machine learning and deep learning for improved attention–deficit hyperactivity disorder diagnosis
• Chapter 15 A coherent review of deep learning techniques for in-depth analysis of electroencephalogram signals
• Chapter 16 Multimodal sentiment analysis: combining bidirectional encoder representations from transformers for text and visual features
• Chapter 17 Social media bigotry detection
• Chapter 18 Challenges and ethics in multimodal healthcare artificial intelligence
• Chapter 19 Improving tumor diagnosis and prognosis through deep learning and medical imaging
• Chapter 20 Navigating risks: a deep dive into healthcare industry analysis
• Chapter 21 Conclusion and future directions in multimodal data fusion in healthcare
• Index