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