Dr. Shrooq Alsenan is an Assistant Professor and the Director of CCIS AI Center at Princess Nourah bint Abdulrahman University. Dr. Shrooq is holding a PhD in Information Systems Sciences from King Saud University specializing on Artificial Intelligence applications in healthcare, medicine and Drug discovery. Her research was awarded "1st place distinguished PhD dissertation" in the Annual Awards Ceremony for Excellence in Scientific Research at King Saud University 2022. She was also recipient of the healthcare innovation research chair grant at KSU.
Dr. Shrooq received two fellowship grants from IBK Program for Saudi women and from MIT Jameel Clinic enabling her to assume the role of a Postdoctoral Massachusetts Institute of Technology (MIT). She worked in Computer Science & Artificial Intelligence Lab (CSAIL) and Jameel Clinic AI & Healthcare Center at MIT.
EDUCATION
PhD in Information Systems Sciences from King Saud University specializing on Artificial Intelligence applications in healthcare
From Annotation to Prediction: Hospital-Grade Early Seizure Risk Prediction from Adult EEG Norah Alharbi, Mashael Aldayel, Shrooq Alsenan, Raneem Alyami, Enas Almowalad, et al. Diagnostics, 2026 Background: Manual review of EEG recordings in clinical settings is inherently time-consuming and labor-intensive. These challenges highlight a pressing need for automated EEG analysis tools capable of supporting clinicians by improving efficiency and diagnostic accuracy. Objectives: This study aims to develop and validate an AI-based model for the automated interpretation of adult EEG recordings. Unlike previous approaches that emphasize seizure detection during ictal states, our model targets the early prediction of seizure risk through systematic annotation and recognition of interictal patterns. Methods: The model is designed to accurately distinguish between normal and abnormal EEGs, encompassing both interictal and ictal activity. Abnormal EEGs will be further classified into three clinically relevant categories: (1) non-epileptiform abnormalities such as focal or diffuse slowing, (2) epileptiform discharges, and (3) electrographic seizures. Three AI-based classification algorithms were implemented: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). Results: RF demonstrated optimal performance across most tasks, achieving 96.50% accuracy for normal activity identification. This AI-driven system enhances the efficiency, consistency, and accessibility of EEG interpretation. It is particularly valuable in settings with limited access to neurophysiologists and offers an innovative approach to improving diagnostic timelines and clinical decision-making. Conclusions: Ultimately, this tool will support physicians in diagnosing neurological conditions and monitoring patient progress over time.
Audit-as-code: a policy-as-code framework for continuous AI assurance Aoun E. Muhammad, Kin-Choong Yow, Shrooq Alsenan Frontiers in Artificial Intelligence, 2026 Introduction Existing AI assurance and governance frameworks rely heavily on documented written policies and manual reviews of the implementation. The primary challenge is not the length of these documents, but to operationalize the gap from transforming qualitative requirements into verifiable controls. This approach makes ensuring continuous compliance through the development life cycle hard to enforce, scale, and reproduce. Methods This study presents a continuous assurance framework called Audit-as-Code that maps governance requirements to technically-auditable rules, that can be a combination of versioned policy specification and executable checks for evidence artifacts, linked to structured evidence regarding data, models, provenance, performance, decisions, and explanations regarding the decisions being made. While the framework addresses the governance and regulatory mapping requirements, the primary focus of this study is MLOps/CI-CD (continuous integration/continuous delivery) operationalization, and turning these requirements into deterministic checks and gate decisions integrated in operational workflows. In this study, we introduce an assured readiness score that integrates the governance risk with other key responsible AI principles, such as traceability and explainability. This approach helps in aligning deployment decisions with predefined risk tiers, and the framework automates decisions on whether a system can proceed, requires remediation and fixes, or should be blocked. It also provides targeted suggestions for improvement and compliance for the lags identified. Results We evaluated this framework on representative AI systems and demonstrated how a single evidence bundle can be used to support assessment across different governance regulations. Discussion In doing so, Audit-as-Code ensures AI assurance transforms from a documentation-driven policy module to a quantitative, auditable, reproducible, and operationally practical module to ensure compliance.
Morphology-guided attention networks for explainable skin cancer detection under clinical uncertainty Muhammad Zaheer Sajid, Muhammad Fareed Hamid, Zepa Yang, Mohammad Alhefdi, Shrooq Alsenan, et al. Frontiers in Oncology, 2026 Accurate and reliable skin cancer detection from dermoscopic images remains challenging due to large visual variability, overlapping lesion appearances, and inherent clinical uncertainty. To address these issues, this work proposes a morphology-guided attention framework for explainable and uncertainty-aware skin lesion classification. The system integrates lesion segmentation to preserve clinically meaningful morphological structures, followed by an attention-based classification network that emphasizes diagnostically relevant regions while suppressing background artifacts. Visual attention and attribution maps are generated to provide transparent explanations aligned with established dermoscopic criteria. In addition, an uncertainty estimation module is incorporated to quantify prediction confidence and identify ambiguous or out-of-distribution cases for safe clinical triage. The proposed approach is evaluated on publicly available dermoscopic datasets and achieves classification accuracy 99.12% with a recall rate above 99% for malignant lesions, demonstrating strong sensitivity for early cancer detection. Experimental results show that morphology-guided attention improves both classification performance and interpretability compared to conventional deep learning models. Furthermore, uncertainty-aware predictions enhance model reliability by reducing overconfident errors in challenging cases. These findings indicate that the proposed framework offers a robust, explainable, and clinically relevant solution for automated skin cancer screening under real-world conditions.
Enhancing transparency understanding using machine learning and visual analytics Samiha Fadloun, Sara Laouadi, Souham Meshoul, Kheireddine Choutri, Shrooq Alsenan, et al. Peerj Computer Science, 2026 In today’s digital age, users are frequently confronted with lengthy terms and conditions documents associated with various products and services. Such documents often reference multiple entities (such as stakeholders, individuals, and users), with certain entities repeated throughout, underscoring their relative importance within the text. This study proposes a novel approach to facilitate the comprehension of terms and conditions by enhancing the detection and weighting of entities, as well as identifying relationships among them. By leveraging machine learning techniques (particularly natural language processing (NLP)) in conjunction with visual analytics, we aim to improve transparency and accessibility. Furthermore, we present an improved version of TranspVis, a visual analytics system to provide a more intuitive representation of transparency-related information. The proposed approach is evaluated through a combination of case studies and user experiments, offering a comprehensive assessment of its utility in rendering complex legal documents more interpretable. The findings underscore the potential of such tools to support large-scale applications in legal domains, with expert feedback affirming the value and relevance of the proposed solution.
A novel deep semantic- and vision-based self-attention architecture for skin cancer classification Junaid Aftab, Muhammad Attique Khan, Sobia Arshad, Amir Hussain, Shrooq Alsenan, et al. Digital Health, 2026 Objectives In the world, skin cancer is a significant health concern, and early diagnosis of this cancer plays a key role in improving patient outcomes. The early detection of this cancer reduces the death rate, but due to the complexity of the diagnosis, incorrect detection and prediction are provided by the experts. Therefore, it is essential to propose a computer-aided diagnostic system based on deep learning and explainable Artificial Intelligence (XAI) techniques that can be used as a second opinion in clinics and help physicians more accurately detect and predict this type of cancer. Methods This work presents the proposed deep learning architecture consisting of two modules—skin lesion segmentation and lesion type classification. The proposed architecture is interpreted using XAI techniques to better evaluate the black-box model. In the skin lesion segmentation phase, we implemented DeepLab V3 architecture for semantic segmentation. The ResNet-18 model was used as the backbone, and later hyperparameters were optimized using Bayesian Optimization (BO). In the classification phase, we design a FusedNet architecture called Inverted self-attention with Vision Transformer (ISAwViT). The proposed fused network combines an inverted self-attention residual architecture with a vision transformer. The proposed fused network extracted feature information more deeply than performing an accurate prediction in a later stage. The design model is trained, and later in the testing phase, extracted features are classified using Softmax and several other classifiers. Results The lesion segmentation and classification experiment was conducted on the HAM10000 dataset. The accuracy achieved by the HAM10000 dataset was 95.16% for lesion segmentation and 97.5% for lesion classification. Conclusion Compared with recent techniques, the proposed model is more effective and efficient. In addition, the interpretation of the proposed model was performed using LIME and Grad-CAM, which show how the fused model makes correct classifications.
