A review of Explainable Artificial Intelligence in healthcare

Smart healthcare, an integral element of connected living, plays a pivotal role in fulfilling a fundamental human need. The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Its multifaceted framework encompasses vital components such as the Internet of Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud computing, as well as next-generation wireless communication technologies. Many research papers discuss smart healthcare and healthcare more broadly. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. This combined effort has not only enhanced the safety of frontline healthcare workers but has also augmented the overall efficacy in managing the pandemic, subsequently reducing its impact on human lives and mortality rates. Remarkable strides have been made in both applications and technology within the IoMT domain. However, it is imperative to acknowledge that this technological advancement has introduced certain challenges, particularly in the realm of security. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy. These encompass a spectrum of concerns, ranging from replay attacks, man-in-the-middle attacks, impersonation, privileged insider threats, remote hijacking, password guessing, and denial of service (DoS) attacks, to malware incursions. In this comprehensive review, we undertake a comparative analysis of existing strategies designed for the detection and prevention of malware in IoT environments.

pdf/progression-and-challenges-of-iot-in-healthcare-a-short-53ob6bgf8s.pdf

Progression and Challenges of IoT in Healthcare: A Short Review

Diagnosing and predicting the outcome of cardiovascular disease are essential tasks in medicine that help ensure patients receive accurate classification and treatment from cardiologists. The use of machine learning in the healthcare sector has grown due to its ability to identify patterns in data. By applying machine learning techniques to classify the presence of cardiovascular diseases, it's possible to decrease the rate of misdiagnosis. This study aims to create a model capable of accurately forecasting cardiovascular diseases to minimize the deaths associated with these conditions. In this paper, two types of SVM model such as linear SVM and polynomial SVM is used. Accuracy, precision, recall and F1 score has been evaluated for comparing linear SVM and polynomial SVM. Polynomial SVM provides better accuracy than linear SVM.

Heart Disease Prediction using SVM

Cloud computing offers a flexible framework in which data and resources are spread across different locations and can be accessed from various industrial environments. This technology has revolutionized the way resources such as data, services, and applications are used, stored, and shared in industrial applications. Over the past decade, industries have rapidly embraced cloud computing due to its advantages of enhanced accessibility, cost reduction, and improved performance. Moreover, the integration of cloud computing has led to significant advancements in the field of the Internet of Things (IoT). However, this quick shift to the cloud has also introduced various security concerns and challenges. Traditional security solutions are not always suitable or effective for cloud-based systems. Despite the continuous use of complex cyber weapons, efforts have been made in recent years to address the security issues and concerns associated with cloud platforms. The rapid progress of deep learning (DL) in the field of artificial intelligence (AI) has provided opportunities to tackle these security challenges in the cloud. The research presented in this study encompasses a comprehensive survey of the enabling architecture, services, configurations, and security models for cloud-based IoT. It also categorizes the security concerns in IoT within four major categories (data, network and service, applications, and people-related security issues) and provides a detailed discussion on each category. Furthermore, the study examines the latest advancements in cloud-based IoT attacks, analyzes significant security issues within each category, and presents the limitations from a broader perspective encompassing general, artificial intelligence, and deep learning aspects.

Security aspects in IoT based cloud computing

This paper introduces BlockNIC, an innovative blockchain infrastructure designed to operate exclusively on SmartNICs. Unlike traditional blockchain implementations, BlockNIC leverages the unique capabilities of SmartNICs to execute relatively simple computations directly on the network path, eliminating the need for additional hardware and reducing reliance on host CPUs. By harnessing idle resources within the network, BlockNIC significantly reduces energy consumption and hardware requirements, addressing the environmental concerns associated with conventional blockchain architectures. Through comprehensive performance comparisons between SmartNICs and bare-metal servers, this study demonstrates the promising potential of BlockNIC in achieving scalability, security, and environmental sustainability in blockchain networks. The findings highlight BlockNIC's ability to enhance overall performance and reliability while minimizing resource limitations, thereby unlocking new possibilities for various applications and use cases previously hindered by energy and hardware constraints. The emergence of BlockNIC aligns with the global sustainability agenda, offering a timely solution to the environmental challenges posed by traditional blockchain technologies. By promoting the adoption of SmartNIC-based blockchain infrastructures, this research contributes to a greener and more secure digital future. It emphasizes the importance of exploring innovative approaches to address the environmental impact of technological innovations, urging researchers, industry professionals, and policymakers to recognize the transformative potential of SmartNIC-based solutions in advancing sustainability and efficiency in blockchain ecosystems.

https://wjarr.com/sites/default/files/WJARR-2024-1026.pdf

Empowering blockchain with SmartNIC: Enhancing performance, security, and scalability

Discussing the use of artificial intelligence (AI) in healthcare, explainability is a highly contentious topic. AI-powered systems may be superior at certain analytical tasks, but their lack of explanation continues to breed distrust. Because the majority of existing AI systems are incomprehensible and opaque, it is unlikely that AI technologies will be properly exploited and incorporated into standard clinical practice. We begin by discussing the present state of XAI development, with a focus on its applications in healthcare. Numerous IoHT-related linked health applications have been examined in XAI to establish their privacy, security, and explainability effectiveness. If we employ clinical decision assistance systems (CDAS) based on artificial intelligence, our approach will combine legal, technological, patient, and medical considerations. To gain a better grasp of the significance of explainability in clinical practice, several disciplines focus on distinct fundamental concerns and values. Explainability must be technically appraised in terms of how it could be attained and what it entails for future development. Important legal checkpoints for explainability include informed consent, certification, and licensing for medical equipment. It is important to look at the relationship between medical AI and people from both the patient’s and the doctor’s points of view. 

Explainable Artificial Intelligence (XAI) with IoHT for Smart Healthcare: A Review

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Explainable Artificial Intelligence (XAI) with IoHT for Smart Healthcare: A Review