Multimodal Data Fusion in Healthcare : AI Approaches for Precision Diagnosis

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予約

Multimodal Data Fusion in Healthcare : AI Approaches for Precision Diagnosis

Singh, Akansha (EDT)/ Dhull, Anuradha (EDT)/ Lamba, Monika (EDT)

ウェブストア価格 ¥36,713（本体¥33,376）
Academic Press Inc（2026/02発売）
外貨定価 US$ 175.00
ポイント 333pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 330 p.
言語 ENG
商品コード 9780443440250

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.

1. Introduction to Multimodal Data in Healthcare: Opportunities and Challenges
2. Foundations of Multimodal Data Fusion: Techniques and Frameworks
3. AI-Driven Multimodal Diagnostics: Revolutionizing Patient Assessment
4. Fusion of Imaging and Genomic Data: Precision Medicine in Oncology
5. Integrating Clinical and Wearable Data: Real-Time Patient Monitoring
6. Deep Learning for Multimodal Data: Algorithms and Applications in Healthcare
7. Reinforcement Learning for Personalized Treatments Using Multimodal Data
8. Natural Language Processing and EHR Data: Enhancing Clinical Decision Support
9. AI Models for Multimodal Brain Imaging: Advances in Neurological Diagnostics
10. Multimodal Fusion for Cardiovascular Disease Prediction and Monitoring
11. Fusion of Molecular and Histopathological Data: AI in Pathology
12. Challenges in Data Integration: Addressing Bias, Privacy, and Interoperability
13. Edge Computing and IoT for Multimodal Health Data Processing
14. Ethics, Regulation, and Future Directions in AI-Driven Multimodal Healthcare
15. Case Studies: Successful Applications of Multimodal Data Fusion in Clinical Practice