Explainable AI and Cybersecurity Techniques for IoT-Based Medical and Healthcare Applications

Submission Deadline: 31 March 2023 Submit to Special Issue

Guest Editors

Prof. Mohamed Esmail Karar, Shaqra University, Saudi Arabia
Prof. Omar Reyad, Sohag University, Egypt
Prof. Abdel-Haleem Abdel-Aty, African Academy of Science, Kenya

Summary

Artificial Intelligence (AI), cybersecurity and the Internet of Things (IoT) enable technologies that can be integrated into medical and healthcare applications. Internet of Medical Things (IoMT) and Internet of Health Things (IoHT) have recently become an attractive topic for many researchers and physicians because these technologies connect all medical devices, sensors, and software applications through online computers and mobile communication networks. For instance, remote patient monitoring can be achieved through wearable sensors and WiFi-Internet connection at home, while medical doctors are tracking the patient health status at hospitals. This clinical situation was effective during the COVID-19 pandemic lockdown. Additionally, Explainable Artificial Intelligence (XAI), a recent competitive trend in AI, focuses on making traditional AI models more intelligible by using the models' decision-making and prediction outputs. The explainability factor gives real models new potential and gives physicians the confidence to interpret the decisions of machine learning (ML) and deep learning (DL) models used in the diagnosis and treatment procedures. Furthermore, privacy and security aspects of patient data should be highly considered in the framework of IoMT and IoT applications.


Keywords

This special Issue aims to compile original research and review articles presenting recent achievements in this field. The following proposed research topics, but are not limited to
• New theories, methods and evaluation metrics of XAI and cybersecurity in medicine
• Advanced XAI techniques for IoMT and IoHT
• Advanced cybersecurity techniques for IoMT and IoHT
• Explainable machine learning and deep learning methods
• Medical image encryption
• Lightweight encryption for IoMT and IoHT
• Smart decision-making in healthcare systems

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