Title: Sorting Medical Imaging Data Using Artificial Intelligence for Improved Diagnostic Pipelines
Authors: Bouthaina Shamekh Kalloub, Samy S. Abu-Naser
Volume: 10
Issue: 7
Pages: 23-29
Publication Date: 2026/07/28
Abstract:
Artificial Intelligence (AI) has become a transformative technology in the field of medical imaging, enabling healthcare systems to manage and analyze large volumes of imaging data more efficiently. Medical imaging modalities such as X-rays, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) generate vast amounts of data that require accurate interpretation for effective diagnosis and treatment planning. Traditional manual analysis can be time-consuming and may lead to variability in diagnostic outcomes. Recent advances in deep learning and machine learning have introduced intelligent approaches for sorting, classifying, and analyzing medical images with high accuracy. This paper explores the role of AI in improving diagnostic pipelines through the automated organization and classification of medical imaging data. It discusses the application of deep learning models, particularly Convolutional Neural Networks (CNNs), in detecting abnormalities, extracting meaningful features, and supporting computer-aided diagnosis systems. Furthermore, the study highlights how AI contributes to faster diagnostic workflows, enhanced decision-making, and improved patient outcomes through early disease detection. The paper also examines current challenges, including data heterogeneity, model interpretability, and computational complexity. The findings demonstrate that AI-based medical imaging systems have significant potential to enhance diagnostic efficiency, reduce human error, and support the future development of intelligent healthcare solutions.