Saudi Cultural Missions Theses & Dissertations
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Item Restricted Impact of Artificial Intelligence Integration in Emergency Department Triage on Waiting Times: A Systematic Review Compared to Conventional Practices in ED Triage.(The University of Sheffield, 2024-09) Alhazmi, Mohammed; Miles, JemieBackground: The global issue of increased patient waiting times in healthcare facilities is a pressing concern, as it can lead to significant patient harm due to delayed access to healthcare. This research proposes the integration of artificial intelligence into emergency department triage systems as a solution to mitigate this issue. Aims: To evaluate the impact of integrating Artificial Intelligence (AI) support tools on waiting times in Emergency Departments through a systematic review of existing literature. Design: A thorough systematic review of the literature was conducted by searching electronic databases and internet search engines, including ScienceDirect, Springer, and PubMed, as well as reference lists. Studies published from January 1, 2019, to May 25, 2024, were included. Articles that did not pertain to AI, interventions that were irrelevant to emergency departments (EDs) or did not provide outcomes related to reducing waiting times either directly or indirectly, or evaluation data were excluded to ensure the quality and relevance of the included studies. Results: The analysis included ten peer-reviewed journals published after January 2019 on integrated Artificial Intelligence (AI) with emergency department triage. Recent findings suggest that integrating artificial Intelligence (AI) models into the emergency department (ED) triage processes can hold significant potential for reducing overcrowding and minimising wait times. Some studies have found that AI reduces waiting times by between 20 seconds and 30 minutes. However, a study found AI to increase waiting times for categories 3 to 5 by 2.75 to 5.33 minutes. Conclusions: This review has highlighted AI's potential to bring innovative solutions to emergency department settings. Implementing these AI-driven solutions has shown promise in enhancing healthcare delivery in the emergency department. However, further research is crucial to refine these models and ensure their practical application, underscoring the importance of continued involvement in the field.44 0Item Restricted The Use of Artificial Intelligence and Machine Learning in Zero Trust Networks(Newcastle University, 2024) Alnadhari, Sultan Majid; Shepherd, CarltonThis paper focuses on the application of Artificial Intelligence (AI) and Machine Learning (ML) within the context of the Zero Trust (ZT) security model to improve Cybersecurity within the ever-evolving digital landscape. Conventional security models that focus on proactively protecting the perimeter and assuming trust within internal networks are often inadequate against these threats. Zero trust can be characterised as a modern approach resulting from the "never trust, always verify" principle; thus, it implies an unceasing process of the users' authentication and access authorisation. Regarding Zero Trust security, this research builds upon the concept by incorporating AI/ML techniques to enhance threat, anomaly, and predictive detection. The first and foremost is the implementation of deep learning models using an optimised Keras framework better suited for the unique dynamics of the Zero Trust environments. Some of these models successfully differentiate and filter network traffic into normal and malicious categories using state-of-the-art features like dropout characters and dense layers. Briefly discuss some problems and solutions, for instance, data shift and model performance decline in conditions that change with time: transfer learning and periodically, for example, perform retraining of the model. Real-world assessments clearly show that incorporating Artificial Intelligence and Machine Learning into the Zero Trust Architectures enhances the capability to identify and mitigate advanced persistent threats and zero-day attacks. Therefore, this research will form a basis for more work in the area of Artificial Intelligence and Cybersecurity by presenting the knowledge required to establish intelligent security systems that can learn to handle new threats as they emerge effectively in real-time. Specifically, the results highlight how these speeds strengthen Zero Trust security solutions against emerging threats.27 0