This systematic review evaluates recent literature from January 2021 to March 2024 on large language model (LLM) applications across diverse medical specialties. Searching PubMed, Web of Science, and Scopus, we included 84 studies. LLMs were applied to tasks such as clinical natural language processing, medical decision support, education, and aiding diagnostic processes. While studies reported benefits such as improved efficiency and, in some specific NLP tasks, high accuracy above 90%, significant challenges persist concerning reliability, ethical implications, and performance consistency, with accuracy in broader diagnostic support applications showing substantial variability, with some as low as 3%. The overall risk of bias in the reviewed literature was considerably low in 72 studies. Key findings highlight a substantial heterogeneity in LLM performance across different medical tasks and contexts, preventing meta-analysis due to a lack of standardized methodologies. Future efforts should prioritize developing domain-specific LLMs using robust medical data and establishing rigorous validation standards to ensure their safe and effective clinical integration. Trial registration: PROSPERO (CRD42024561381).

The emerging Cloud of Things (CoT) paradigm promises to meet the diverse requirements of many real-world applications, which previously could not be fulfilled by either Cloud Computing or Internet of Things (IoT). Trading CoT resources is a challenging aspect, particularly when managing Quality of Service (QoS) as resource providers and application developers have different priorities. This paper focuses on the challenge of supporting QoS when trading CoT resources and performing resource allocation. The contributions of this paper are 1) the problem of managing QoS while trading CoT resources is investigated as an optimisation problem 2) a QoS model is proposed to solve the problem by optimising five different QoS objectives 3) experimental evaluation of the proposed model using three optimisation algorithms. The evaluation results show the efficiency and dynamism of the proposed model in optimising CoT resource allocation based on diverse QoS objectives including resource cost, energy consumption, response time, fault tolerance and resource coverage.

Cloud Computing represents a new era where computing is offered as a service rather than as a physical product. The next level of flexibility will be achieved when Cloud Computing services can be automatically traded. This paper focuses on providing the foundation for simple and flexible Cloud resource trading. This is achieved by proposing vocabularies for the trading of Cloud resources and algorithms for a Cloud marketplace. A multi-attribute combinatorial marketplace is proposed as a solution for situations where Cloud resources need to be traded in combination (bundles). Vocabularies are introduced to serve as a foundation to build standards for Cloud resources trading.

Road damage detection and assessment are crucial components of infrastructure maintenance. However, detecting multiple crack types in a single image remains a challenging limitation, particularly at varying scales. This is due to the lack of road datasets with various types of damage on varying scales. To address this challenge, we introduce: 1) A novel dataset called the Diverse Road Damage Dataset (DRDD) that captures multiple damage types per individual image. 2) A new detection model called RDD4D that exploits Attention4D blocks to enable better feature refinement across multiple scales. The Attention4D module processes feature maps through an attention mechanism combining positional encoding and talking head components to extract both local and global contextual features. In our comprehensive experimental analysis comparing various state-of-the-art models, our enhanced model demonstrates superior performance in detecting large-sized road cracks with an Average Precision (AP) of 0.458 and maintained competitive performance with an overall mAP of 0.446 on our proposed dataset. Moreover, we also provide results on the CrackTinyNet dataset; our model achieved around a 0.21 increase in performance. The code, model weights, dataset, and our results are publicly available at https://github.com/msaqib17/Road_Damage_Detection.

Cloud Computing and Internet of Things (IoT) have evolved to meet the requirements of many real-world applications. Many of these requirements cannot be fulfilled by using either technology separately. In order to fulfil such diverse requirements, the integration of Cloud Computing and IoT is emerging as a new paradigm called Cloud of Things (CoT). CoT is expected to host heterogeneous resources and fulfil complex requirements of resource providers and consumers. This complexity poses a real challenge for resource allocation in CoT. To tackle this challenge, resource allocation is described as a trading optimisation problem and utility functions are used to rank candidate resource allocation assignments. The contributions of this paper are 1) introducing vocabularies needed for trading CoT resources 2) proposing a marketplace system architecture to enable efficient trading of CoT resources 3) examining the use of different utility functions to rank candidate resource assignments 4) performing simulations to validate the effectiveness of the proposed approach using three optimisation algorithms.