Textbook in PDF format

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.
Explainable AI is currently on a rapid rise for biomedical and healthcare applications. Because of its advantages in dealing with big, complex amounts of data, explainable AI concepts are applied in many fields and as a critical one, the medical field has a remarkable interest in the use of that sub-field of Artificial Intelligence. Thanks to the use of Machine Learning, vision, and Deep Learning techniques, many improvements have been done in terms of medical data analysis, diagnosis, treatment, and even personal healthcare. There are already many positive results provided by Deep Learning, in the literature of medicine. The advent of 5G technology and the exponential rise in connected devices are anticipated to make it more difficult to allocate network resources in a reliable and efficient manner. It is hypothesized that current advancements in Artificial Intelligence and Machine Learning might provide a solution to the problems associated with the black-box model of learning where the output predicted or the conclusion yielded by the machine is hidden from the user. Therefore, it is anticipated that the explainable artificial intelligence-driven components of future networks would be highly relied upon, which might make them a valuable target for assault.
This book will concentrate on the application of network attacks-driven intelligent computing approaches, the state-of-the-art, cutting-edge discoveries, and current developments in AI/ML algorithms because of new technologies and quicker user-device connection. A variety of ideas and approaches are being researched and developed in this interesting and developing multidisciplinary area of 5G networks to address difficult and complicated issues. Network analysis, machine learning, computer vision, and deep learning-enabled assessment of the suggested solutions are likely to be included in applications-oriented development. More instances of the possible usage of issues are provided throughout the book, along with probable solutions.
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.
Preface
1 Exploring explainable AI: Techniques and comparative analysis
2 Introduction to explainable artificial intelligence in biomedical and healthcare applications
3 Smart healthcare system: Automated methods for diagnosis of diseases using digital twin technology
4 Explainable AI unlocks the potential of AI in biomedical research and practice
5 An intuitive ensemble modelling with X-AI architecture for autism classification
6 Mental disorder management using explainable artificial intelligence
7 Unlocking insights: Data analysis and processing empowered by explainable AI
8 Revolutionizing healthcare: The role of artificial intelligence in transforming eHealth care
9 Mental disorders management using explainable artificial intelligence (XAI)
10 Machine learning approach to predict adverse effects of mRNA vaccination: A comparative study of classification models and ensemble learning techniques
11 Explainable artificial intelligence (EAI): For healthcare applications and improvements
12 Challenges and imperatives for equitable and ethical development of explainable AI in healthcare
13 A comprehensive analysis of the convergence between deep learning technologies and bioinformatics, catalyzing groundbreaking innovations in biological data interpretation
14 An exhaustive exploration of explainable AI-driven applications in healthcare, enhancing diagnostic accuracy, treatment efficacy, and patient trust
15 An in-depth exploration of data analysis and processing through the prism of explainable artificial intelligence paradigms
16 Implications of artificial intelligence in disease diagnosis