SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
Browse
13 results
Search Results
Item Restricted Saudi Students’ Experiences Using Artificial Intelligence to Support Well-Being While Studying Abroad(University of Sheffield, 2024-09) Barhyem, Smer; Rowsell, JenniferThis desk-based study explores Saudi Students’ Experiences Using Artificial Intelligence to Support Well-Being while studying abroad. It focuses on their challenges to investigate how AI can address them, through a qualitative approach. Data were collected through secondary sources highlighting Saudi master's students' challenges studying abroad and the impact of AI on their well-being. The data were analyses by using thematic analysis to determine meaningful themes. The research discovers that AI can offer helpful solutions, by providing language support, promoting social integration, and offering mental health services. Despite the possible benefits, there are some concerns about ethical issues related to AI, such as privacy, breaches, and biases. This research seeks to encourage Saudi master's students to use AI by explaining how it offers multiple services and the impact on their well-being and suggesting some recommendations. These recommendations include the opportunity to Invest in AI support systems in the universities to enhance their language and encourage them to communicate with others and develop mental health support services, leading to starting the treatment quickly and offering academic support services. Additionally, by considering these recommendations universities can create supportive environments for Saudi master's students studying abroad, leading to improved well-being and academic success.26 0Item Restricted Exploring the Applications of Artificial Intelligence in Enhancing Pre-Hospital Care: A Scoping Review(Queen’s University, Belfast, 2024) Alfaifi, Yahya; Clarke, SusanArtificial Intelligence (AI) has the potential to significantly improve pre-hospital care, especially in emergency medical services (EMS). However, its current application remains scattered, with varying integration levels across care stages. This scoping review aims to map and assess existing research on AI applications within pre-hospital care without focusing on specific AI technologies, such as machine learning (ML), deep learning (DL), or decision support systems (DSS). The review reflects the current research landscape, capturing how AI is utilised across critical stages such as call-taking, dispatch, and on-scene assessment. Using the framework developed by Arksey and O’Malley (2005), a systematic search was conducted across multiple databases to identify studies relevant to AI in pre-hospital care. The scope was deliberately broad to capture a comprehensive view of the available literature, focusing on identifying areas where further research is needed. The findings indicate that DSS is commonly used to support decision-making in call-taking and dispatch, while more advanced AI applications like ML and DL show potential in predictive analytics and real-time decision-making. However, these technologies are still in their early stages of real-world implementation. This review highlights the gaps in AI research, particularly in the later stages of prehospital care, such as transport and handover. Further exploration is necessary to unlock AI’s full potential in enhancing EMS operations and outcomes.38 0Item Restricted AI GENERATED TEXT VS. HUMAN GENERATED TEXT(University of East Anglia, 2024-09) Hadi, Nedaa; Misri, KazhanThe ability to distinguish between AI-generated and human-generated texts is becom- ing increasingly critical as AI technologies advance. This dissertation explores the development and evaluation of various machine learning models to accurately classify text as either AI-generated or human-generated. The research aims to identify the most effective classification techniques and preprocessing methods to enhance model performance and generalization across different text datasets. A range of machine learning and deep learning models, including Support Vec- tor Machine (SVM), Random Forest, Logistic Regression, Decision Tree, BERT, and LSTM, were employed to evaluate their effectiveness in distinguishing between the two types of texts. The study utilized a balanced and representative dataset through data sampling and augmentation techniques. Key preprocessing steps were implemented to refine the input data, and hyperparameter tuning was conducted to optimize model performance. The generalization capabilities of the models were further tested on additional datasets with varying text characteristics. The findings revealed that SVM and Random Forest models achieved the highest accuracy and reliability in classifying texts, demonstrating strong performance across multiple evaluation metrics. In contrast, deep learning models like BERT and LSTM were less effective under the given conditions, suggesting a need for more extensive datasets and computational resources to leverage their full potential. These results highlight the strengths and limitations of different approaches to text classification, providing a foundation for future research to enhance AI detection in diverse applications.21 0Item Restricted The Role of Artificial Intelligence in Breast Cancer Screening Programmes: A Literature Review and Focus Upon Policy Implications(The University of Edinburgh, 2024) Alrabiah, Alanoud; Hellowell, MarkBackground: Breast cancer (BC) is a leading cause of morbidity and mortality amongst older women, leading to the introduction of screening programmes to support earlier detection and improved survivability. Current screening programmes rely upon the performance of radiologists in terms of accuracy; however, evidence shows that both under and overdiagnosis means screening also results in harms to some women. Artificial intelligence is then a promising technology for improving the accuracy of mammogram screening. Aim: To describe the potential roles of AI in BC screening, and the potential benefits, limitations and risks in these roles. Methods: PubMed, SCOPUS, and CINAHL were searched. Primary research studies published in English and in the last ten years, investigating the accuracy of AI systems for screening BC, were eligible for review. Evidence was appraised using the CASP (2024) checklists and data analysed narratively. Results: 14 studies were found eligible for review, mostly adopting a retrospective study design or laboratory study design. Roles for AI in BC screening include as a standalone system replacing radiologists entirely, as risk stratification systems used before radiologist readings, or as reader aids. While some studies reported AI systems to be superior, others reported accuracy to be inferior to radiologist readings. Differences in results could be due to variations in AI system or radiologist performance. Conclusion: There is insufficient evidence to support the use of AI in BC screening programmes, and more robust, prospective studies comparing readings from clinical practice are urgently required. Policy must also be implemented to regulate the use of AI until there is sufficient evidence to support its use.12 0Item Restricted Knowledge, Use, and Confidence in Artificial Intelligence Applications Among Orthodontists in the UK and Ireland(The University of Edinburgh, 2024) Sabbagh, Abdulrahman; McGuinness, NiallBackground: Artificial intelligence (AI) has been applied in orthodontics using different applications, including cephalometric tracing, remote and initial assessment, remote monitoring of treatment progress, and extraction decision-making. This study aims to assess knowledge, usage, confidence, and future interest in AI applications amongst orthodontists in the United Kingdom and Ireland. Materials and Methods: A cross-sectional study was conducted amongst orthodontists in the United Kingdom and Ireland. A self-reported questionnaire was used. Data was collected on participant demographics, as well as knowledge, usage, confidence, and future use of different AI applications. Pearson chi-square tests were used to assess if demographics, region of work, sector of work, and years of experience influenced responses. Results: A total of 331 responses were received. There was a general awareness that AI can be used in orthodontics in 80.4% of respondents. In addition, the overall mean knowledge, usage, and confidence levels of the examined AI applications were 51.3%, 16.6% and 18.7% respectively. Knowledge, usage, and confidence levels for specific AI applications differed, with the greatest familiarity, usage, and confidence observed in AI applications for cephalometric tracing and remote monitoring. Alternatively, the lowest awareness, usage, and confidence were attributed to AI applications that assisted in identifying the need for extractions. Additionally, most orthodontists (81%) consider AI to be beneficial for future use and the majority (96.7%) were open to learning about it. Statistically Significant associations (P >0.05) were discovered between knowledge, usage, and confidence in various AI applications and between multiple factors including healthcare sectors, practice regions, and gender. Conclusion: This study revealed differing levels of knowledge, usage, and confidence in various AI applications among practitioners in the UK and Ireland. The findings suggest a knowledge-implementation gap that might be beneficial to be targeted by educational means to increase the adoption of AI technology in the orthodontic practice.73 0Item Restricted Machine Learning for Improved Detection and Segmentation of Building Boundary(Cardiff University, 2022-09-27) Algarni, Salem; Mourshed, MonjurThis thesis addresses the need for rapid assessment of damaged assets, such as buildings, following natural or man-made disasters. Traditional manual visual analysis is labor-intensive and time-consuming, prompting the exploration of automated detection methods using multi-source geospatial data. The research reviews existing object detection methods and introduces two novel post-processing techniques. Artificial intelligence, particularly convolutional neural networks (CNNs), is employed to improve building detection accuracy. The study emphasizes the superior performance of CNN architectures, especially Region-based Convolutional Neural Network (Mask R-CNN), which enhances semantic detection and boundary recognition. Mask R-CNN, combined with conditional random fields (CRFs), effectively identifies and refines building contours in satellite images. The proposed post-processing techniques modify the relative orientation properties of buildings and integrate key points from two neural networks to adjust predicted contours using innovative snap algorithms. The results demonstrate notable improvements in boundary detection accuracy, with enhancements of 2.5% in F1-Score, 4.6% in Intersection over Union, and 1% in overall pixel accuracy compared to current state-of-the-art methods. The thesis highlights the potential of CNNs in automating image processing tasks, learning complex concepts from raw data, and aiding infrastructure planning and disaster response.39 0Item Restricted Artificial Intelligence Performance on Digital Mammography, Synthetic Mammography, and Digital Breast Tomosynthesis: A Systematic Review.(Saudi Digital Library, 2023-11-23) Alamri, Abdulrahman; Polycarpou, Irene; Feltbower, RichardObjective This review aims to evaluate the effectiveness of AI in breast cancer screening by examining its performance across various modalities, including digital mammography (DM), digital breast tomosynthesis (DBT), and synthetic mammography (SM), as well as its ability to detect different breast densities and types of lesions. Method The Medline and Scopus databases were searched electronically from the earliest date to 9 August 2023 for studies that met the inclusion criteria. A QUADAS-2 tool was used to assess the quality of the included studies. The diagnostic performance metrics of included studies were reported, such as sensitivity, specificity, accuracy, and the area under the curve (AUC). A narrative synthesis with tables and figures was used to present the result. Results Based on the search, 13 studies were included. Most studies were published in 2021 (46%) and in the USA (4/13). A majority of the included studies used cross-sectional datasets to test their AI tools, and the USA was found to be the most common dataset location. Two studies developed AI tools to classify breast density, and the mean accuracy was 82% for DM and 80% for SM, whereas the AUC were 0.94 and 0.93, respectively. The remaining 11 studies focused on AI diagnostic applications. Four DM studies showed a sensitivity (mean: 73.7, median: 69.9, IQR: 14), two SM studies (mean: 79.9), and five DBT sensitivity studies (mean: 82.2, median: 84, IQR: 21). Conclusion AI in breast classification has a comparable performance, while the DBT has the highest performance among modalities and techniques.66 0Item Restricted Barriers and Facilitators of AI-Driven Tools in Healthcare Project Management in Saudi Arabia: An Exploratory Study.(Saudi Digital Library, 2023-12-11) Alyami, Khawlah; Pagano, RosaneThe aim of this research is to identify and analyse the primary barriers and facilitators that could influence the project management of healthcare AI initiatives in Saudi Arabia. By doing so, the study seeks to contribute to the existing body of knowledge and assist in formulating strategies and policies that can expedite the digital transformation process in the healthcare sector. The research employs a qualitative methodology, utilizing semi-structured interviews and focus groups as tools for data collection. The participants included healthcare project coordinators and senior project leads with substantial experience in the field. The research aims to fill a gap in the existing literature by offering insights into the unique factors impacting AI technology adoption in the Saudi healthcare sector. Findings from the study identify strategic initiatives, skill development, and collaborations as central facilitators in the integration process. Simultaneously, data management challenges, ethical and regulatory concerns, stakeholder management, and risk management are recognized as significant barriers. The study offers specific recommendations to address these barriers, focusing on improving data security and compliance with regulations. This research serves as a valuable resource for stakeholders in the healthcare sector, providing actionable insights to advance the integration of AI tools in Saudi Arabia, aligning with the objectives of Vision 2030.42 0Item Restricted The perception and Knowledge of Healthcare professionals towards the use of AI tools: A case from Saudi Arabia.(Saudi Digital Library, 2023-12-01) Alhumaid, Haya; Kanis, FatimaIn recent days, Artificial intelligence tools in healthcare are widely implemented around the world, this leads the healthcare professionals to face a new obstacle in their careers. This study aims to explore healthcare professionals’ perceptions and attitudes toward the use of AI tools in Saudi healthcare. The study is an attempt of systematic literature review, literature search was conducted by using google scholar, PubMed. An inclusion and exclusion criteria had been used to extract 14 relevant studies all based in Saudi Arabia and MMAT tool is used for assessing the quality. The findings indicates that Most of the studies reported the majority of healthcare professionals in Saudi Arabia have the basic knowledge of AI. However, their knowledge and acceptance might be affected by some demographic and personal factors. Higher educational level and experience can be correlated with more understanding of AI but it does not necessary correlate with better acceptance of AI tools. The study also indicates that some specialists e.g., Radiologists are more threatened by the presence of AI compared the other specialists, and this might be due higher advancement of AI in their fields and the nature of their job. Furthermore, most of the studies reported that a considerable number of participants who have concerns of being replace by AI. lack of proper training was strongly noticed among the studies, and most participants are willing to improve their skills of using clinical AI. In conclusion, while there are a lot of optimistic views of clinical AI, lack of trust was strongly noticed among health professionals. A proper training and education are crucial to ensure the health professionals advancement in using clinical AI.45 0Item Restricted A Systematic Review of the Role of Artificial Intelligence Technology in Sustainable Supply Chain(Saudi Digital Library, 2023-12-14) Abuhaimed, Noura; Touboulic, AnneIn recent years, the sustainable supply chain (SSC) has gained considerable global attention due to its incorporation of sustainability dimensions into its processes. Recent advancements in technologies have presented solutions to various issues associated with supply chain networks (SC). While the role of various technologies like blockchain, IoT, and Industry 4.0 in sustainable supply chains (SSCs) is well- established in the literature, there has been a lack of research on the role of Artificial Intelligence (AI) technology and its sub-fields in establishing SSCs. To address this gap, this paper pursues the following objectives: (1) To conduct a systematic review of the role of Artificial Intelligence technology in sustainable supply chains. For this, a total of 55 articles were searched using a set of keywords in the Scopus database in all research fields published in 45 peer-reviewed academic journals from 2010 to 2022. These papers were filtered using the title abstract keywords principle. (2) To document the association of AI technology with the social, and environmental aspects of the supply chain. (3) To highlight the AI deployments that could contribute to the enhancement of SSCs. (4) To draw attention to the gap in the literature and identify promising areas for future study. (5) To create a conceptual framework for the role of AI technology in SSC. This research is valuable for academic scholars and industry practitioners seeking insights into the role of AI in SSCs, as it is the first paper to comprehensively present all AI types in SSC. The study demonstrates that AI technology has the capacity to enhance supply chain sustainability, and it foresees a growing popularity of AI in supply chain management. By laying a strong foundation for understanding the role of AI technology in advancing sustainability, the results offer recommendations for future research directions to address the identified gaps in the literature. This study presents a research framework that integrates AI- based techniques with conventional supply chain practices to address supply chain challenges effectively. The framework outlines the incorporation of AI-enabled operations alongside operations that do not rely on AI to achieve sustainability within the supply chain. The developed framework could help researchers and professionals who want to create SSC models using AI methods32 0