Explainable Artificial Intelligence in Medical Decision Support Systems

Agbotiname Lucky Imoize is a lecturer in the Department of Electrical and Electronics Engineering at the University of Lagos, Nigeria and a research scholar at Ruhr University Bochum, Germany. He is a Fulbright fellow, a senior member of the IEEE, and a registered engineer. He has published over 90 articles and conference papers, two edited textbooks, and five book chapters. His research interests are 6G wireless communication, artificial intelligence, and the Internet of Things. Jude Hemanth is a professor at Karunya University, Coimbatore, India. He has delivered more than 100 keynote talks and invited lectures at international conferences and workshops, and has authored more than 100 research papers and 27 edited books. His research areas include computational intelligence and image processing. Dinh-Thuan Do is a research scientist in the Department of Electrical Engineering, University of Colorado Denver, USA. He won a Golden Globe Award from the Vietnam Ministry of Science and Technology in 2015. He has published over 200 journal and conference papers, one textbook, two edited books and eight book chapters. His research interests include signal processing in wireless communications networks, full-duplex transmission, and energy harvesting. Samarendra Nath Sur is an assistant professor in the Department of Electronics & Communication Engineering at Sikkim Manipal Institute of Technology, India. His research interests include wireless communication, signal processing, and remote sensing. He has published more than 77 SCI/Scopus indexed international journal and conference papers, four edited textbooks, and five book chapters. He is a senior member of the IEEE and associate member of the Institution of Engineers (India) (IEI).

Chapter 1: Explainable artificial intelligence (XAI) in medical decision systems (MDSSs): healthcare systems perspective
Chapter 2: Explainable artificial intelligence (XAI) in medical decision support systems (MDSS): applicability, prospects, legal implications, and challenges
Chapter 3: Explainable Artificial Intelligence-based framework for medical decision support systems
Chapter 4: Prototype interface for detecting mental fatigue with EEG and XAI frameworks in Industry 4.0
Chapter 5: XAI for medical image segmentation in medical decision support systems
Chapter 6: XAI robot-assisted surgeries in future medical decision support systems
Chapter 7: Prediction of erythemato squamous-disease using ensemble learning framework
Chapter 8: Security-based explainable artificial intelligence (XAI) in healthcare system
Chapter 9: Explainable dimensionality reduction model with deep learning for diagnosing hypertensive retinopathy
Chapter 10: Understanding cancer patients with diagnostically influential factors using high-dimensional data embedding
Chapter 11: Explainable neural networks in diabetes mellitus prediction
Chapter 12: A KNN and ANN model for predicting heart diseases
Chapter 13: Artificial Intelligence-enabled Internet of Medical Things for COVID-19 pandemic data management
Chapter 14: A deep neural network for the identification of lead molecules in antibiotics discovery
Chapter 15: Statistical test with differential privacy for medical decision support systems
Chapter 16: Automated decision support system for diagnosing sleep diseases using machine intelligence techniques
Chapter 17: XAI methods for precision medicine in medical decision support systems
Chapter 18: The psychology of explanation in medical decision support systems