Explainable Artificial Intelligence Methodologies for Medical Applications based on Internet of Health Things (IoHT)

Submission Deadline: 30 December 2023 Submit to Special Issue

Guest Editors

Dr. Mazin Abed Mohammed, University of Anbar, Iraq.
Prof. Seifedine Kadry, Norrof University College, Norway.
Dr. Oana Geman, Universitatea Stefan cel Mare din Suceava, Romania.


These days, many technologies have emerged in different domains such as 5G that enable wireless communication and cloud ubiquitous services accessible from everywhere. The technologies motivate the Internet of Health Things (IoHT), which convert human physical monitoring based on machine monitoring. These new healthcare systems are geographically distributed and have many concerns, such as patient data security, anomaly avoidance, services cost, delay, mobility and automation. However, existing healthcare systems or methods are fixed or statics. These complex dynamic problems cannot be solved with the existing techniques. Therefore, dynamic uncertainty with time and space complexity in ubiquitous poses many research challenges. Thus, this special issue well all intelligent techniques, approaches, and architectures based on Explainable Artificial Intelligence (XAI) for Medical Applications. XAI is a widely researched environment where a healthcare system is developed based on dynamic techniques such as supervised, unsupervised, reinforcement learning, deep learning, and neural network. In XAI, the incremental approaches, iterative approaches and novel approaches widely welcome to solve the dynamic problem of IoHT These methods can integrate with the offloading problem, application partitioning problem, task scheduling problem, resource allocation problem, and graph problems.


The main objective of this special issue is to bring together diverse, novel and impactful research work on Explainable Deep Learning for Medicine based on The Internet of Health Things, thereby accelerating research in this field.


• XAI for Internet of Medical Things
• XAI methodologies to detecting emerging medical threats from healthcare data
• XAI and Medical data fusion
• Health Intervention Design, Modeling and Evaluation based on IoHT
• Biomedical Ontologies, Knowledge Graphs based on IoHT
• Real-time Explainable AI for medical image and data processing
• feature selection for interpretable XAI classification
• XAI and Chronic disease management based IoHT
• XAI and COVID-19 Detection and Classification based IoHT- systems
• Deep Neural Networks for medical image detection, recognition, and segmentation
• Deep Neural Networks for cancer diagnosis
• Future directions of intelligent XAI medical imaging in healthcare
• Use cases for XAI in medical imaging
• XAI methods for computer-aided detection in ultrasound/CT/MRI
• Online database and webserver based on XAI and IoHT in bioinformatics and biomedical images
• XAI for biomedicine, genomics, proteomics, transcriptomics, metabolomics, and sociogenomics
• XAI and IoHT for Patient tracking, prediction and monitoring
• Quantum Computing for Medical Data
• Deep Learning for Medical Applications
• Adaptive Machine Learning for Healthcare Applications
• Blockchain Technology form Medical data
• Data Science for Big Data Anylytics
• Internet of Medical things Applications
• Intelligent Telehealth Monitoring with XAI
• Trustworthy AI and Its enginnering Applications
• XAI for Industrial Internet of Things
• XAI and Cybersecurity Techniques for IoT-Based Medical Applications
• XAI for Smart City

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