SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted REGULATING ALGORITHMIC DISCRIMINATION UNDER THE EU AI ACT: EVALUATING BIAS MITIGATION DUTIES FOR HIGH-RISK AND GENERAL-PURPOSE AI SYSTEMS(Saudi Digital Library, 2025) ALSOMALI, ABDULAZIZ; ZIHAO, LIAlgorithmic systems now allocate work, credit, welfare and even police attention (facial recognition systems). They are not ‘neutral’ instruments; they often reproduce and amplify structural disadvantage. This dissertation asks whether the European Union’s Artificial Intelligence Act, when coupled with the Charter of Fundamental Rights and the equality acquis, can prevent and redress such discrimination. This dissertation argues that the Act is normatively necessary but only conditionally sufficient. Its risk architecture, data‑governance duties, documentation and oversight requirements, and the upstream regime for general‑purpose models supply the right legal levers. Constitutional adequacy will materialise only if implementation embeds equality law into technical practice through three cumulative conditions: (i) standards that require context‑specific metric selection justified by proportionality and the availability of less discriminatory alternatives; (ii) supervision with genuine statistical and legal capacity across the system lifecycle; and (iii) remedial pathways that convert logs and technical files into proof under burden‑shifting rules. Thus this paper turns to a functional comparison with the United States and the United Kingdom shows how adverse‑impact doctrine, discovery, and regulator‑led guidance can be harnessed without sacrificing the coherence of the EU model. Followed by Chapter 5 which sets out a concise implementation blueprint and measurable indicators. On that basis, bias mitigation is framed not as ethics, but as a legal duty by which the Act’s success must be judged.9 0Item Restricted Machine Learning for Radiotherapy Treatment of Prostate Cancer(Saudi Digital Library, 2026) Alqarni, Maram; Teresa, Guerrero Urbano; Andrew, KingExternal beam radiotherapy (EBRT) and brachytherapy (BT) are both forms of radiation treatment used for prostate cancer to destroy cancer cells. EBRT applies the radiation externally while BT involves placing radioactive seeds inside the prostate. At Guy’s Cancer Centre, both treatment modalities are performed depending on various factors. Each of the treatment modalities involves different imaging modalities used for treatment planning, delivery and follow-up. However, both have some overlapped clinical tasks such as defining the clinical target volume (CTV) and organs at risk (OARs) from imaging data. The work described in this thesis aims to perform research to promote clinical translation of machine learning (ML) techniques to streamline workflows in EBRT and BT. The first piece of work in this thesis focuses on an ML-based segmentation model for prostate MRI. One of the main challenges affecting clinical adoption of ML in MRI segmentation is the domain shift problem. The findings of this piece of work reveal for the first time the significant impact on model performance of using different acquisition/annotation protocols, even if using the same scanner vendor/field strength. It is shown that training an ML model with data that covers the important sources of domain shift can produce a robust model with good generalisability performance. The next piece of work investigates the possibility of race bias in ML-based prostate MRI segmentation. Through experiments on a controlled dataset of White and Black patients, it is shown that the model performance gap between Black and White subjects is dependent on the level of (im)balance between Black and White subjects in the training data. Again, it is shown that training using demographically balanced data can produce a fair and robust model. The conclusion from both of these pieces of work is that model performance can be robust if the training data is sufficiently diverse, both in terms of image characteristics and patient demographics. Building upon these analyses, the thesis next investigates the clinical utility of a diagnostic prostate MRI model trained on diverse data and externally validates it on in-house clinical data. The evaluation of this model encompasses not only standard quantitative metrics but also measurement of inter-observer variability in manual segmentation and assessments of performance on downstream clinical tasks. Next, the thesis investigates the clinical utility of multi-organ ML-based segmentation models. Here, two models are investigated: one for planning MRI called the “FIMRAa-P” model and another radiotherapy CT model called the “PelvisMA-CT” model. Both models are extensively evaluated quantitatively and qualitatively by five observers. The agreement between the quantitative metrics and the qualitative clinical metrics is also investigated for each clinical structure, revealing generally poor agreement between the two. It is also shown that this agreement is dependent on the structure being segmented and the profession of the clinicians who perform the evaluations. One of the main clinical translation outcomes of this thesis is the deployment of PelvisMA-CT by the Clinical Scientific Computing (CSC) group at GSTFT, and its integration into a contouring application called GSTTAutoSeg. This model is currently being used clinically at Guy’s Cancer Centre and the thesis presents the results of a monitoring and enhancement study based on its ongoing clinical use. Overall, the thesis presents a number of key contributions, all aimed at promoting clinical translation of ML in EBRT and BT. It is hoped that the work performed will accelerate the benefits of ML in radiotherapy treatment planning and delivery and ensure that all patients benefit from the introduction of the thoroughly evaluated new technology.8 0Item Restricted The Role of Artificial Intelligence in Optimising Demand Forecasting and Inventory Management Within Pharmaceutical Supply Chain Management - A case Study of Saudi Arabia(Saudi Digital Library, 2026) Albandari, Alenize; Mulyata, JohnThe study examined the application of Artificial Intelligence (AI) in optimising demand forecasting and inventory management within the pharmaceutical supply chain in Saudi Arabia. The literature review indicates that AI has significant potential to improve forecasting accuracy and inventory efficiency; however, its adoption is still hindered by technical, organisational, and regulatory challenges. The existing literature highlights important gaps; however, most studies have focused on developed markets, while research on emerging economies, such as Saudi Arabia, remains limited. As the nation undergoes a remarkable digital transformation with a set of Vision 2030 initiatives, issues with AI technologies are emerging in response to deeper systemic inefficiencies in healthcare logistics. The study is based on secondary data, employing a qualitative research design that involves a thematic analysis of literature, industry reports, and related case studies. The inquiry was guided by four research questions: what leads to the adoption of AI, how much AI can affect demand forecasting, whether it can be effective in enhancing inventory management, and what are the obstacles to successful implementation. The study revealed that AI improves the precision of demand forecasting by 30% and reduces the forecasting errors by 20-50%. In inventory management, AI is expected to lead to a 20% reduction in waste and a 25-35% decrease in inventory expenses. Challenges persist, including poor data, outdated systems, organisational resistance, cultural norms, and regulatory ambiguity. It is concluded that technological and strategic preparedness is high; nevertheless, to achieve real success, it is essential to address the organisational and regulatory barriers that run deep. Some of the recommendations include investing in data infrastructure, enhancing AI literacy, developing more transparent regulatory frameworks, and promoting intersectoral cooperation.20 0Item Restricted Evaluating Static, Contextual, and End-to-End Embedding Techniques for Malware Detection on Dynamic API Call Data(Saudi Digital Library, 2026) Basfar, Mohammed Raed; Joey, LamThe rate of malware development continues to challenge cybersecurity, with traditional signature- and heuristic-based techniques overwhelmed by polymorphic and zero-day attacks. Natural language processing (NLP) offers a promising direction by modeling dynamic API call sequences as semantic linguistic data, enabling sophisticated embedding and sequence-learning methods to be used for malware detection. This dissertation contrasts and analyzes three typical embedding methods static, contextual, and end-to-end task-learned representations—under a shared experimental framework. Specifically, it employs Word2Vec embeddings with a Convolutional Neural Network (CNN), contextual BERT embeddings with a CNN, and a Bidirectional Long Short-Term Memory (BiLSTM) network with a trainable embedding layer and weighted loss function to address class imbalance. The experiments were conducted on a dynamic API call dataset of around 44,000 malware and 1,000 benign samples, summarized by the first 100 API calls executed under sandboxed conditions. Results indicate that the Word2Vec + CNN pipeline had the highest overall accuracy and malware detection precision but the lowest benign recall. The BERT + CNN model provided more balanced class performance, but at the expense of added computational overhead. The BiLSTM had the highest benign recall, as it was able to easily distinguish from non-malicious activity, but the lowest precision and hugely added resource use. The findings point out the competing trade-offs among detection accuracy, benign recall, and processing efficiency, highlighting the issue of aligning model selection with actual security contexts' resource constraints and priorities. The study contributes by reporting a comparative systematic review of the embedding approaches for malware detection and offering informative insights into performance vs. efficiency trade-offs. Apart from its scientific significance, it proves the larger potential of NLP-based approaches to supporting malware detection systems and to informing the design of responsive, resource-aware cybersecurity systems.17 0Item Restricted The Impact of Digital Transformation on Enhancing the Medical Supply Chain(Saudi Digital Library, 2025) Abdulaziz, Albaraa; Theo, FotisThe current thesis aims to investigate how digital transformation (DT) technologies, artificial intelligence (AI), the Internet of Things (IoT), and blockchain can be used to improve the medical supply chain in Saudi Arabia through the Vision 2030 metric. A systematic review based on PRISMA was used to identify and screen the studies (n=6); narrative and thematic synthesis were performed as part of the study, and three general themes were created: operational excellence, economic viability, and implementation readiness. According to the findings, DT enhances visibility, predictability, responsiveness, and resilience, but the strength of evidence was not consistent, and some of the studies have indicated a high implementation cost and organisational barriers. It is important to note that the synthesis has revealed the convergence as well as tensions among studies that technical interoperability and workforce readiness are important in the effective adoption of DT. Some of these recommendations are gradual execution of technology, capacity building initiatives and sponsorship of national policy to enable equal adoption in all the facilities. Its implications go beyond Saudi Arabia, and the fact that DT serves as an agent of predictive and agile systems and the global discussions about how digital health transformation strategies are to be implemented.6 0Item Restricted The Impact of Artificial Intelligence on Supply Chain Optimization in Saudi Arabia(Saudi Digital Library, 2025) AlQahtani, Abdullah Saeed; Khobzi, HamidThis dissertation examines the impact of Artificial Intelligence (AI) on supply chain optimization in Saudi Arabia, with particular emphasis on its alignment with the Kingdom’s Vision 2030 objectives. AI technologies such as machine learning, predictive analytics, robotic process automation, and the Internet of Things are increasingly recognized for their potential to enhance efficiency, resilience, and sustainability within supply chain operations. However, despite growing national interest, empirical research focusing on AI adoption in the Saudi supply chain context remains limited. The study adopts a qualitative, interpretivist approach based on multiple secondary case studies drawn from peer-reviewed literature published between 2020 and 2025. The analysis is guided by the Technology–Organization–Environment (TOE) framework, supported by the Supply Chain Operations Reference (SCOR) model, to examine both adoption drivers and process-level applications across key sectors, including telecommunications, healthcare, manufacturing, and national mega-projects. Findings indicate that AI adoption in Saudi supply chains is most advanced in planning, forecasting, and logistics delivery, while challenges persist in system integration, data quality, workforce readiness, and organizational resistance to change. Environmental factors such as Vision 2030 initiatives and government support act as strong enablers, although adoption remains concentrated among large organizations and flagship projects. The study concludes that while AI has significant potential to transform Saudi supply chains, its full benefits depend on improved digital integration, skills development, and supportive policy frameworks.63 0Item Restricted Unblocking Operational Excellence: The Role of Digital Transformation in Optimising the Oil and Gas Supply Chains(Saudi Digital Library, 2025) AlObaidi, Danah; Jappie, AGDigital transformation is increasingly recognised as a systemic enabler of operational excellence in oil and gas supply chains, where volatility, and regulatory demands exceed traditional efficiency tools. This study investigates how four technologies -the Internet of Things (IoT), Artificial Intelligence (AI), blockchain, and digital twins- contribute through alignment. Using a qualitative, interpretivist approach, a systematic literature review and thematic analysis of 36 peer-reviewed studies identified five themes: predictive and AI-enabled performance, real-time visibility, integration bottlenecks, blockchain for compliance, and organisational fitness. Results show that integration maturity consistently outperforms novelty in driving value, while tensions such as explainability versus latency and governance versus agility shape adoption outcomes. The dissertation advances a conceptual framework linking digital enablers to Operational excellence through a “stacked capability” logic and offers practical recommendations for oil and gas leaders. Ultimately, digital transformation emerges not as a linear solution but as a discipline of orchestration that enables reliable, scalable, and trusted operational performance.20 0Item Embargo Comparative Study of the Performance of an Artificial Intelligence Platform in Detecting Periapical Radiolucencies Across Different Imaging Modalities(Saudi Digital Library, 2025) Allihaibi, Marwa; Koller, Garrit; Mannocci, FrancescoAim: This thesis aimed to evaluate the diagnostic accuracy of a commercial artificial intelligence (AI) platform in detecting periapical radiolucencies (PARLs) across different imaging modalities. The evaluation included preoperative assessment of teeth requiring primary endodontic treatment with comparison against dental professionals, radiographic healing assessment at follow-up, and assessment of teeth referred for apical microsurgery. Methods: Five retrospective diagnostic accuracy studies were conducted to evaluate the commercial AI platform Diagnocat (versions 1.0 and 2.0) for PARL detection across multiple imaging modalities. The studies utilised radiographic data from patients treated at Guy's and St Thomas' NHS Foundation Trust between 2012-2023. The study sample included: (1) 339 teeth indicated for primary root canal treatment, assessed on periapical radiographs (PARs) and compared with two experienced endodontists; (2) 376 teeth assessed at minimum one-year follow-up on PARs for radiographic healing outcomes, compared with two endodontists; (3) 134 molars evaluated on cone-beam computed tomography (CBCT) for preoperative and postoperative assessment; (4) 177 posterior teeth requiring primary endodontic treatment, assessed on PARs and compared with eleven general dental practitioners (GDPs); and (5) 116 anterior teeth referred for apical microsurgery, evaluated on both PARs and CBCT. Reference standards varied by study design: CBCT for PAR validation, expert consensus for CBCT assessment, and histopathology for cases referred for apical microsurgery. Statistical analyses included calculation of sensitivity, specificity and accuracy with 95% confidence intervals. McNemar's test assessed diagnostic performance differences. Subgroup analyses examined performance across anatomical variables. Results: Across five retrospective studies, Diagnocat demonstrated significant performance variability dependent on imaging modality, anatomical location, and treatment status. On PARs, for non-root-filled teeth requiring primary root canal treatment, sensitivity was 47.9% and specificity 95.4%, indicating reliable exclusion of disease but missing over half of actual lesions. In root-filled teeth assessed at one-year follow-up, sensitivity increased to 67.3% while specificity decreased to 82.3%, suggesting altered diagnostic thresholds based on treatment status. Performance on CBCT scans of molars showed marked improvement, achieving 93.9% sensitivity and 65.2% specificity in preoperative cases, and 88.6% sensitivity and 63.3% specificity in follow-up cases. While three-dimensional (3D) imaging substantially enhanced sensitivity for posterior teeth, it was accompanied by reduced specificity, indicating potential for overdiagnosis. Anatomical analysis revealed consistent underperformance in maxillary teeth and specific roots on PARs, limitations that were largely resolved on CBCT for posterior teeth. In contrast, anterior teeth demonstrated persistently poor performance regardless of imaging modality, achieving only 63.8% sensitivity on PARs and 57.5% on CBCT despite histopathological confirmation of periapical pathology. Cross-modality consistency was poor, with only 43.8% of lesions detected on both imaging modalities. Compared to clinicians, Diagnocat showed lower sensitivity (47.9% vs 65.3%) but comparable specificity (95.4% vs 97.7%) when assessed against endodontists in non-root-filled teeth. In root-filled teeth, this pattern reversed, with the AI achieving higher sensitivity (67.3% vs 49.3%) but lower specificity (82.3% vs 92.5%). When compared with GDPs, Diagnocat demonstrated lower sensitivity (44.9% vs 80.8%) but markedly superior specificity (94.3% vs 47.5%). Re-evaluation with version 2.0 showed no improvement in PARL detection across 1,308 PARs and 268 CBCT scans. Conclusion: This thesis demonstrated that multiple factors critically determine AI diagnostic accuracy for PARL detection, including imaging modality, anatomical location, and treatment status, thus highlighting fundamental limitations in training data representation and model development. AI platforms require comprehensive training on datasets with balanced anatomical representation and the incorporation of three-dimensional imaging before being considered for reliable implementation in endodontic diagnosis.17 0Item Restricted AI-Based Analysis of Magnetic Nanoparticle Relaxometry Curves for Structure-Specific Cancer Detection and Classification(Saudi Digital Library, 2025) AlHumam, Malack; Hovorka, OndrejCancer remains one of the world’s leading causes of death, and the key to successful treatment relies heavily on early and accurate diagnosis. This thesis explores a minimally invasive diagnostic method by combining magnetorelaxometry (MRX) with artificial intelligence (AI). Magnetorelaxometry measures how magnetic nanoparticles relax after being excited by an external magnetic field, producing relaxation curves that depend on anisotropy orientation and variation, particle number, structure geometry. Among magnetic nanoparticles, superparamagnetic iron oxide nanoparticles (SPIONs) are particularly suited for biomedical applications due to their biocompatibility and tunable relaxation properties. However, these curves often overlap and appear indistinguishable to the human eye, making traditional analysis challenging. The central research question of this thesis is whether AI can classify nanoparticle ensembles by structure and particle number from their relaxation curves, using them as unique markers for cancer detection and classification. To address this, five simulated datasets were generated, each incorporating multiple structures with different particle numbers under varying anisotropy conditions. After preprocessing, the data were analyzed with supervised, semi-supervised, and unsupervised models, supported by dimensionality reduction visualizations (PCA, t-SNE, UMAP). Supervised models achieved the strongest performance, with multiclass logistic regression reaching an accuracy of 0.89 in the dataset with aligned anisotropy and no variation. ZChains consistently emerged as the most distinguishable ensembles, relaxing roughly twice as long as YChains and providing clearer separability in both geometry and particle number, as confirmed by PCA scatter plots. In contrast, YChains frequently collapsed under z-axis anisotropy alignment, while Triangles and Rings were distinguishable only under controlled anisotropy variation. Arkus structures degraded rapidly when anisotropy variation increased. Semi-supervised pseudo-labeling maintained comparable accuracy of 0.817 under limited labeling, while unsupervised KMeans clustering, although non-predictive, provided insights into ensemble overlap and natural similarity groupings. The main contribution of this work is the demonstration that AI can classify nanoparticle ensembles through relaxation curve morphology rather than biomarker binding assays. This represents a shift from proof of detection toward structure-based classification, bridging magnetic physics with biomedical AI applications. Future directions include aligning anisotropy axes experimentally, exploring relaxation saturation for cancer staging, and translating AI pipelines to real biological magnetorelaxometry data.15 0Item Restricted Artificial Intelligence in Armed Conflict: Responsibility and Legal Reforms to Ensure Compliance with International Humanitarian Law(Saudi Digital Library, 2025) AlJerais, Najd; Master’s, degreeThe growing integration of artificial intelligence (AI) into military operations presents significant and unique challenges for international humanitarian law (IHL). This dissertation aims to answer the question of the extent to which AI-enabled weapons challenge compliance with IHL, and how effective potential reforms are in addressing these challenges. It first explores current and emerging military AI technologies and the application of IHL, analysing the risks posed to the core principles of distinction, proportionality, and precaution. It then identifies the main compliance challenges introduced by the deployment of AI-enabled weapons in the conduct of hostilities. These include the weakening of human judgment, responsibility gaps in both state and individual responsibility within the chain of command, and the fragmentation caused by the absence of international consensus. Finally, the dissertation considers potential responses, focusing on strengthening Article 36 weapons review procedures under Additional Protocol I, implementing human rights and ethical oversight, and developing enforceable international standards to ensure meaningful human control. It argues that while IHL remains the central legal framework governing armed conflict, it requires urgent strengthening through binding legal, ethical, and governance reforms. Ultimately, the discussion contributes to ongoing debates on the future of warfare by clarifying the limits of the IHL framework, evaluating the effectiveness of proposed reforms, and highlighting the need for a coherent regulatory approach capable of responding effectively to rapid technological change.22 0