In recent years, the Internet of medical things (IoMT) has become a significant technological advancement in the healthcare sector. This systematic review aims to identify and summarize the various applications, key challenges, and proposed technical solutions within this domain, based on a comprehensive analysis of the existing literature. This review highlights diverse applications of the IoMT, including mobile health (mHealth) applications, remote biomarker detection, hybrid RFID-IoT solutions for scrub distribution in operating rooms, IoT-based disease prediction using machine learning, and the efficient sharing of personal health records through searchable symmetric encryption, blockchain, and IPFS. Other notable applications include remote healthcare management systems, non-invasive real-time blood glucose measurement devices, distributed ledger technology (DLT) platforms, ultra-wideband (UWB) radar systems, IoT-based pulse oximeters, accident and emergency informatics (A&EI), and integrated wearable smart patches. The key challenges identified include privacy protection, sustainable power sources, sensor intelligence, human adaptation to sensors, data speed, device reliability, and storage efficiency. The proposed mitigations encompass network control, cryptography, edge-fog computing, and blockchain, alongside rigorous risk planning. The review also identifies trends and advancements in the IoMT architecture, remote monitoring innovations, the integration of machine learning and AI, and enhanced security measures. This review makes several novel contributions compared to the existing literature, including (1) a comprehensive categorization of IoMT applications, extending beyond the traditional use cases to include emerging technologies such as UWB radar systems and DLT platforms; (2) an in-depth analysis of the integration of machine learning and AI in IoMT, highlighting innovative approaches in disease prediction and remote monitoring; (3) a detailed examination of privacy and security measures, proposing advanced cryptographic solutions and blockchain implementations to enhance data protection; and (4) the identification of future research directions, providing a roadmap for addressing current limitations and advancing the scientific understanding of IoMT in healthcare. By addressing current limitations and suggesting future research directions, this work aims to advance scientific understanding of the IoMT in healthcare.

Purpose: Medical technologies and assets are one of the main drivers of increasing healthcare cost. The rising number and complexity of medical equipment have forced hospitals to set up and regulate medical equipment management programs to ensure critical devices are safe and reliable. The purpose of this paper is to gain insights into maintenance management-related activities for medical equipment. The paper proposes applying a tailored reliability-centered maintenance (RCM) approach for maintenance activities selection for medical equipment. Such approach will support assets management teams in enhancing operation, decrease risk and cost, and ultimately improve health of patients served by these equipment. Design/methodology/approach: The traditional RCM approach will be used with a focus on criticality reduction. By criticality, the authors refer to the severity of failures and occurrence. The proposed method relies on the use of reliability growth analysis for opportunity identification followed by a thorough failure mode and effect analysis to investigate major failure modes and propose ways to reduce criticality. The effectiveness of the proposed method will be demonstrated using a case of one of the leading obstetric and gynecological hospitals in United Arab Emirates and in the Gulf Cooperation Council region. Findings: The case examines the relationship between the current practice of planned preventive maintenance and the failure rates of the equipment during its life span. Although a rigorous preventive maintenance program is implemented in the hospital under study, some critical equipment show an increasing failure rates. The analysis highlights the inability of traditional time-driven preventive maintenance alone in preventing failures. Thus, a systematic RCM approach focused on criticality is more beneficial and more time and cost effective than traditional time-driven preventive maintenance practices. Practical implications: The study highlights the need for utilizing RCM approach with criticality as the most important prioritization criterion in healthcare. A proper RCM implementation will decrease criticality and minimize the risk of failure, accidents and possible loss of life. In addition to that, it will increase the availability of equipment, and reduce cost and time. Originality/value: This paper proposes a maintenance methodology that can help healthcare management to improve availability and decrease the risk of critical medical equipment failures. Current practices in healthcare facilities have difficulty identifying the optimal maintenance strategy. Literature focused on medical maintenance approach selection is rather limited, and this paper will help in this discussion. In addition to that, the Association for the Advancement of Medical Instrumentation supports the initiative of adopting RCM on a large scale in healthcare. Therefore, this paper address the gap in the literature for medical equipment maintenance and the work is in line with the recommendation of leading healthcare association. The paper also presents statistical review of the total number of received maintenance work orders during one full year in the hospital under study. The analysis supports the need for more research to examine current practice and propose more effective maintenance approaches.

Photovoltaic systems (PV) have been extensively used worldwide as a reliable and effective renewable energy resource due to their environmental and economic merits. However, PV systems are prone to several environmental and weather conditions that impact their performance. Amongst these conditions is dust accumulation, which has a significant adversative impact on the solar cells’ performance, especially in hot and arid regions. This study provides a comprehensive review of 278 articles focused on the impact of dust on PV panels’ performance along with other associated environmental factors, such as temperature, humidity, and wind speed. The review highlights the importance of modelling dust accumulation along with other ecological factors due to their interactive nature, and the differences between cleaning techniques and schedules effectiveness. Moreover, the study provides a review of statistical and artificial intelligence models used to predict PV performance and its prediction accuracies in terms of data size and complexity. Finally, the study draws attention to several research gaps that warrant further investigation. Among these gaps is the need for proper dynamic optimisation models for cleaning schedules and a more advanced machine and deep learning models to predict dust accumulation while considering environmental and ageing factors.