Explainable Artificial Intelligence for Biomedical and Healthcare Applications
eBook - ePub

Explainable Artificial Intelligence for Biomedical and Healthcare Applications

  1. 302 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Explainable Artificial Intelligence for Biomedical and Healthcare Applications

About this book

This reference text helps us understand how the concepts of explainable artificial intelligence (XAI) are used in the medical and healthcare sectors. The text discusses medical robotic systems using XAI and physical devices having autonomous behaviors for medical operations. It explores the usage of XAI for analyzing different types of unique data sets for medical image analysis, medical image registration, medical data synthesis, and information discovery. It covers important topics including XAI for biometric security, genomics, and medical disease diagnosis.

This book:

• Provides an excellent foundation for the core concepts and principles of explainable AI in biomedical and healthcare applications.

• Covers explainable AI for robotics and autonomous systems.

• Discusses usage of explainable AI in medical image analysis, medical image registration, and medical data synthesis.

• Examines biometrics security-assisted applications and their integration using explainable AI.

The text will be useful for graduate students, professionals, and academic researchers in diverse areas such as electrical engineering, electronics and communication engineering, biomedical engineering, and computer science.

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Yes, you can access Explainable Artificial Intelligence for Biomedical and Healthcare Applications by Aditya Khamparia,Deepak Gupta in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Biomedical Science. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half-Title
  3. Series
  4. Title
  5. Copyright
  6. Contents
  7. Preface
  8. About the book
  9. Editors
  10. List of contributors
  11. 1 Exploring explainable AI: Techniques and comparative analysis
  12. 2 Introduction to explainable artificial intelligence in biomedical and healthcare applications
  13. 3 Smart healthcare system: Automated methods for diagnosis of diseases using digital twin technology
  14. 4 Explainable AI unlocks the potential of AI in biomedical research and practice
  15. 5 An intuitive ensemble modelling with X-AI architecture for autism classification
  16. 6 Mental disorder management using explainable artificial intelligence
  17. 7 Unlocking insights: Data analysis and processing empowered by explainable AI
  18. 8 Revolutionizing healthcare: The role of artificial intelligence in transforming eHealth care
  19. 9 Mental disorders management using explainable artificial intelligence (XAI)
  20. 10 Machine learning approach to predict adverse effects of mRNA vaccination: A comparative study of classification models and ensemble learning techniques
  21. 11 Explainable artificial intelligence (EAI): For healthcare applications and improvements
  22. 12 Challenges and imperatives for equitable and ethical development of explainable AI in healthcare
  23. 13 A comprehensive analysis of the convergence between deep learning technologies and bioinformatics, catalyzing groundbreaking innovations in biological data interpretation
  24. 14 An exhaustive exploration of explainable AI-driven applications in healthcare, enhancing diagnostic accuracy, treatment efficacy, and patient trust
  25. 15 An in-depth exploration of data analysis and processing through the prism of explainable artificial intelligence paradigms
  26. 16 Implications of artificial intelligence in disease diagnosis
  27. Index