Artificial Intelligence Technology in Healthcare by Sharma, Neha; Srivastava, Durgesh; Sinwar, Deepak;

Rövid leírás:

This book explains recent breakthroughs and problems in deep learning security and privacy issues, emphasizing current state-of-the-art methods, methodologies, implementation, attacks, and countermeasures. It examines the issues related to develop AI-based security mechanisms which can gather or share data across healthcare applications.

Hosszú leírás:

Artificial Intelligence Technology in Healthcare: Security and Privacy Issues focuses on current issues with patients’ privacy and data security including data breaches in healthcare organizations, unauthorized access to patients’ information, and medical identity theft. It explains recent breakthroughs and problems in deep learning security and privacy issues, emphasizing current state-of-the-art methods, methodologies, implementation, attacks, and countermeasures. It examines the issues related to developing artifiicial intelligence (AI)-based security mechanisms which can gather or share data across several healthcare applications securely and privately.

Features:

• Combines multiple technologies (i.e., Internet of Things [IoT], Federated Computing, and AI) for managing and securing smart healthcare systems.








• Includes state-of-the-art machine learning, deep learning techniques for predictive analysis, and fog and edge computing-based real-time health monitoring.








• Covers how to diagnose critical diseases from medical imaging using advanced deep learning-based approaches.








• Focuses on latest research on privacy, security, and threat detection on COVID-19 through IoT.








• Illustrates initiatives for research in smart computing for advanced healthcare management systems.

This book is aimed at researchers and graduate students in bioengineering, artificial intelligence, and computer engineering.

Tartalomjegyzék:

1. Artificial Intelligence in Healthcare: A Paradigm Shift. 2. AI’s Implication in Healthcare and Medical Systems. 3. Advancements of Artificial Intelligence in Healthcare. 4. A Review of Deep Learning Applications in Modernized Healthcare Services. 5. An Empirical Evaluation of Learning Models for the classification of Fall Detection dataset. 6. Review in Healthcare using Augmented Reality/Virtual Reality: An IoT perspective. 7. Shooting method for solving two-point boundary value problems in ODEs numerically and applications to medical science. 8. Key Management in Healthcare using IoMT. 9. Security issues related to COVID data using Artificial Intelligence (AI). 10. Security Issues and Defense Mechanism Using IoMT. 11. Threat Modeling in Healthcare Systems. 12. Use of Block Chain Technology for Privacy and Threat Detection. 13. Blockchain based Decentralized Biometric Authentication System for Vulnerability Analysis. 14. Security Issues Related to Cervical Cancer Research: A Bibliometric Analysis.