Deep learning approaches have demonstrated great achievements in the field of computer-aided medical image analysis, improving the precision of diagnosis across a range of medical disorders. These developments have not, however, been immune to the appearance of adversarial attacks, creating the possibility of incorrect diagnosis with substantial clinical implications. Concurrently, the field has seen notable advancements in defending against such targeted adversary intrusions in deep medical diagnostic systems. In the context of medical image analysis, this article provides a comprehensive survey of current advancements in adversarial attacks and their accompanying defensive strategies. In addition, a comprehensive conceptual analysis is presented, including several adversarial attacks and defensive strategies designed for the interpretation of medical images. This survey, which draws on qualitative and quantitative findings, concludes with a thorough discussion of the problems with adversarial attack and defensive mechanisms that are unique to medical image analysis systems, opening up new directions for future research. We identified that the main problems with adversarial attack and defense in medical imaging include dataset and labeling, computational resources, robustness against target attacks, evaluation of transferability and adaptability, interpretability and explainability, real-time detection and response, and adversarial attacks in multi-modal fusion. The area of medical imaging adversarial attack and defensive mechanisms might move toward more secure, dependable, and therapeutically useful deep learning systems by filling in these research gaps and following these future objectives.

The widespread adoption of Internet of Things (IoT) technologies across various domains has given rise to the Internet of Medical Things (IoMT), which has significantly enhanced the accuracy and capabilities of electronic devices in producing reliable results applicable to the healthcare industry. To leverage the potential of IoMT in healthcare, a series of interconnected events must take place, starting with edge devices collecting data, followed by data aggregation, processing, and informed decision-making based on data analysis. This review article stems from a collaborative and innovative project conducted by participants in the digital economy, organized by the Department of Software Engineering at Tsinghua University in 2021. The project focused on implementing technologies in various fields, with specific teams dedicated to healthcare. During this project, several gaps were identified, and solutions centered around the IoT were proposed. In this comprehensive review, we extensively investigated IoMT services and applications and emphasized how these applications can be optimally implemented to unlock their potentials. Our survey encompassed over 300 research papers, that examined the implementation of IoMT in domains such as Pharmacy Management and Health Insurance Management. Additionally, we analyzed the key enablers and barriers to the successful implementation of IoMT in recent times. To provide a practical perspective, we presented a feasible case study that applied deep learning to IoMT, considering the security concerns associated with its implementation. Furthermore, we identified future research directions and potential areas of improvement based on the gaps identified from the reviewed literatures. By undertaking this review, we aim to contribute to a deeper understanding of IoMT services and applications, shedding light on their optimal utilization within the healthcare industry. Ultimately, our goal is to facilitate advancements in IoMT implementation and to pave the way for enhanced healthcare delivery and improved patient outcomes.

Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19 (COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce the clinician burden. There is no doubt that low resolution, noise and irrelevant annotations in chest X-ray images are a major constraint to the performance of AI-based COVID-19 diagnosis. While a few studies have made huge progress, they underestimate these bottlenecks. In this study, we propose a super-resolution-based Siamese wavelet multi-resolution convolutional neural network called COVID-SRWCNN for COVID-19 classification using chest X-ray images. Concretely, we first reconstruct high-resolution (HR) counterparts from low-resolution (LR) CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super-resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest X-ray image. Exploiting a mutual learning approach, the HR images are passed to the proposed Siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. We validate the proposed COVID-SRWCNN model on public-source datasets, achieving accuracy of 98.98%. Our screening technique achieves 98.96% AUC, 99.78% sensitivity, 98.53% precision, and 98.86% specificity. Owing to the fact that COVID-19 chest X-ray datasets are low in quality, experimental results show that our proposed algorithm obtains up-to-date performance that is useful for COVID-19 screening.