Buch, Englisch, 440 Seiten, Format (B × H): 191 mm x 235 mm
Principles, Technologies, and Applications
Buch, Englisch, 440 Seiten, Format (B × H): 191 mm x 235 mm
ISBN: 978-0-443-36434-1
Verlag: Elsevier Science
Revolutionizing Digital Healthcare Through Artificial Intelligence and Automation: Principles, Technologies, and Applications drives development and improves the digital healthcare environment by leveraging the advantages that Artificial Intelligence (AI) and smart devices offer to the smart healthcare ecosystem. It not only includes principles, technologies, and applications related to the smart healthcare field, but also explores AI-embedded applications in smart devices, sensors, and IoT applications that are widely used worldwide for smart healthcare ecosystems. Across 18 chapters, the volume describes the principles, models, roles, and cutting-edge technologies within the digital healthcare ecosystem. It guides readers from the basics to the practical applications of AI, robotics, and AR/VR technologies in smart healthcare ecosystems. The title emphasizes the harmonization between human intelligence and artificial intelligence, not only in the healthcare sector but also across various aspects of human life. This integration contributes to the popularity of AI in the era of digital healthcare. Furthermore, the volume enhances literacy, particularly in medical healthcare, fosters 21st-century skills, keeps pace with the latest research developments, and applies new methodologies and technology to practical scenarios. The book provides nuanced knowledge that serves as a role model for creating new discoveries among medical industry specialists, academic researchers, scientists, and engineers in the fields of Medical, Healthcare, and Metaverse-based Healthcare.
Autoren/Hrsg.
Weitere Infos & Material
1. Principles and Models of Digital Healthcare Ecosystem
2. The Role of Artificial Intelligence (AI) and Generative AI in Digital Healthcare
3. An Extensive Analysis of AI and IoT for Modern Healthcare Realm
4. Application of AI and Gen AI in Digital Healthcare Ecosystem
5. Advancements and Applications in the Smart Healthcare Ecosystem
6. Enhancing Healthcare Services with AI and Gen AI Technologies
7. Integrating AI and IoT for Smart Healthcare Systems
8. IoT-Based Technologies for Smart Healthcare System
9. Automation and Robotics in Medical Laboratory Services
10. Sensor Technologies and Applications for Smart Healthcare System
11. 6G Communication Network for Smart Healthcare System
12. AR and VR Technologies for Smart Healthcare System
13. Challenges in Implementing Quantum Technology in Healthcare Systems
14. AI-based Blockchain for Smart Healthcare System
15. A New Neural Network Methods for the Detection of Heart Diseases
16. Artificial Internet of Things (AIoT)-Assisted Disease Diagnosis Model for Intelligent Healthcare System
17. Machine Learning for Cardiac Rehabilitation through the Internet of Medical Things in Smart Healthcare System
18. Identify and Classify Brain Tumors Using Deep Learning Techniques and Magnetic Resonance Imaging (MRI) Images
19. Data Mining for Fractal Measurements Features Related Breast Tumor Analysis
20. Artificial Intelligence (AI) and Machine Learning (ML) in Modern Healthcare Ecosystem
21. Recent Advances in the Development and Application of Biosensors for the Detection of Endocrine Disruptor
22. Optimizing ML Algorithms for Real-Time Privacy-Preserving Patient Monitoring in Embedded Systems
23. Digital Edge Detection Algorithms for Accurate Pneumonia Detection in Chest Radiographs
24. Employing AI-Powered Clinical Decision Support to Revolutionize Healthcare
25. Ergonomic Designs in Smart Pills and Biosensors for Optimizing User Experience
26. Medical Laboratory Monitoring and Total Quality Management for a Smart Medical Laboratory
27. Robotics Automation for Smart Healthcare System and Medical Laboratory Service
28. Big Data Analytics for Smart Healthcare System
29. Cybersecurity and Privacy Data Associated with Services of Smart Healthcare
30. Transforming Healthcare from Telemedicine to AI: Principles, Models, Future Prospects, Challenges, and Barriers in the Digital Ecosystem
31. Artificial Intelligence (AI) Techniques for Biosensor Data Analysis
32. Integration of AI and AR in Medical Imaging: A Quantitative Analysis of Workflow Optimization and Diagnostic Performance
33. Machine Learning for Biosensor Signal Processing
34. Revolutionizing Medical Imaging and Diagnostics through Advanced AI and Augmented Reality Technologies
35. Future Prospects, Challenges and Ethics of Cutting-Edge Technologies in Smart Healthcare System
