Principles and Methods of Explainable Artificial Intelligence in Healthcare

  • 5h 41m
  • Akash Kumar Bhoi, Alfonso González Briones, P Naga Srinivasu, Victor Hugo C de Albuquerque
  • IGI Global
  • 2022

Explainable artificial intelligence is proficient in operating and analyzing the unconstrainted environment in fields like robotic medicine, robotic treatment, and robotic surgery, which rely on computational vision for analyzing complex situations. Explainable artificial intelligence is a well-structured customizable technology that makes it possible to generate promising unbiased outcomes. The model’s adaptability facilitates the management of heterogeneous healthcare data and the visualization of biological structures through virtual reality. Explainable artificial intelligence has newfound applications in the healthcare industry, such as clinical trial matching, continuous healthcare monitoring, probabilistic evolutions, and evidence-based mechanisms.

Principles and Methods of Explainable Artificial Intelligence in Healthcare discusses explainable artificial intelligence and its applications in healthcare, providing a broad overview of state-of-the-art approaches for accurate analysis and diagnosis. The book also encompasses computational vision processing techniques that handle complex data like physiological information, electronic healthcare records, and medical imaging data that assist in earlier prediction. Covering topics such as neural networks and disease detection, this reference work is ideal for industry professionals, practitioners, academicians, researchers, scholars, instructors, and students.

About the Author

Victor Hugo C. de Albuquerque has a PhD in Mechanical Engineering with emphasis on Materials from the Federal University of Paraíba (2010), an MSc in Teleinformatics Engineering from the Federal University of Ceará (2007), and he graduated in Mechatronics Technology at the Federal Center of Technological Education of Ceará (2006). He is currently Assistant VI Professor of the Graduate Program in Applied Informatics at the University of Fortaleza (UNIFOR). He has experience in Computer Systems, mainly in the research fields of: Applied Computing, Intelligent Systems, Visualization and Interaction, with specific interest in Pattern Recognition, Artificial Intelligence, Image Processing and Analysis, as well as Automation with respect to biological signal/image processing, image segmentation, biomedical circuits and human/brain-machine interaction, NeuroBiofeedback, Neurorehabilitation, Visual Stimulation, including Augmented and Virtual Reality Simulation Modeling for animals and humans. Additionally, he has research at the microstructural characterization field through the combination of non-destructive techniques with signal/image processing and analysis, and pattern recognition.

P. Naga Srinivasu procured his bachelor degree in computer science engineering in the year 2011 from JNTUK and Masters in Computer Science Technology in 2013 from Gitam University, Visakhapatnam. He is currently a research scholar in Gitam University and his areas of research include Biomedical Imaging, Image Enhancement, Image Segmentation, Object Recognition, Image Encryption.

Akash Kumar Bhoi (B.Tech, M.Tech, Ph.D) is currently associated with KIET Group of Institutions, India as Adjunct Faculty and Directorate of Research, Sikkim Manipal University as Adjunct Research Faculty. He is appointed as the honorary title of “Adjunct Fellow” Institute for Sustainable Industries & Liveable Cities (ISILC), Victoria University, Melbourne, Australia for the period from 1 August 2021 to 31 July 2022. He is also working as a Research Associate at Wireless Networks (WN) Research Laboratory, Institute of Information Science and Technologies, National Research Council (ISTI-CRN) Pisa, Italy. He was the University Ph.D. Course Coordinator for “Research & Publication Ethics (RPE) at SMU.” He is the former Assistant Professor (SG) of Sikkim Manipal Institute of Technology and served about 10 years. He is a member of IEEE, ISEIS, and IAENG, an associate member of IEI, UACEE, and an editorial board member reviewer of Indian and International journals. He is also a regular reviewer of reputed journals, namely IEEE, Springer, Elsevier, Taylor and Francis, Inderscience, etc. His research areas are Biomedical Technologies, the Internet of Things, Computational Intelligence, Antenna, Renewable Energy. He has published several papers in national and international journals and conferences. He has 130+ documents registered in the Scopus database by the year 2021. He has also served on numerous organizing panels for international conferences and workshops. He is currently editing several books with Springer Nature, Elsevier, and Routledge & CRC Press. He is also serving as Guest editor for special issues of the journal like Springer Nature and Inderscience.

Alfonso González Briones holds a Ph.D. in Computer Engineering from the University of Salamanca since 2018, his thesis obtained the second place in the 1st SENSORS+CIRTI Award for the best national thesis in smart cities (CAEPIA 2018). At the same university, he obtained his Bachelor of Technical Engineer in Computer Engineering (2012), Degree in Computer Engineering (2013), and Masters in Intelligent Systems (2014). Alfonso was Project Manager of Industry 4.0 and IoT projects in the AIR Institute, Lecturer at the International University of La Rioja (UNIR), and also “Juan De La Cierva” Postdoc at University Complutense of Madrid. Currently, he is Assistant Professor at the University of Salamanca in the Department of Computer Science and Automatics. He has published more than 30 articles in journals, more than 60 articles in books and international congresses and has participated in 10 international research projects. He is also Member of the scientific committee of the Advances in Distributed Computing and Artificial Intelligence Journal (ADCAIJ) and British Journal of Applied Science & Technology (BJAST) and Reviewer of international journals (Supercomputing Journal, Journal of King Saud University, Energies, Sensors, Electronics or Applied Sciences, among others). He has participated as Chair and Member of the technical committee of prestigious international congresses (AIPES, HAIS, FODERTICS, PAAMS, KDIR).

In this Book

  • Foreword
  • Preface
  • Deep Learning Model for Diagnosing Diabetes
  • Analysis of Cardiovascular Disease Prediction Using Model-Agnostic Explainable Artificial Intelligence Techniques
  • Role of Deep Learning in Medical Image Super-Resolution
  • Analyzing and Forecasting of COVID-19 Situation Using FbProphet Model Algorithms
  • Role of Explainable Artificial Intelligence (XAI) in Prediction of Non-Communicable Diseases (NCDs)
  • Drug Discovery With XAI Using Deep Learning
  • Development of Machine Learning Models for Healthcare Systems Using Python—Machine Learning Models for COVID-19
  • Topical Repute on Artificial Intelligence-Based Approaches in COVID-19 Supervision—Distinct Kiwngpin on Drug Re-Purposing Blueprint
  • An Efficient Multi-Layer Perceptron Neural Network-Based Breast Cancer Prediction
  • A Model-Based Approach for Extracting Emotional Status From Immobilized Beings Using EEG Signals
  • Basic Issues and Challenges on Explainable Artificial Intelligence (XAI) in Healthcare Systems
  • Principles and Methods of Explainable Artificial Intelligence in Healthcare—Framework for Classifying Alzheimer’s Disease Using Machine Learning
  • Compilation of References