Evaluation of the Impact of Artificial Intelligence on Clinical Practice of Radiology in Saudi Arabia.

Background: Artificial Intelligence (AI) is becoming integral to the health sector, particularly radiology, because it enhances diagnostic accuracy and optimizes patient care. This study aims to assess the awareness and acceptance of AI among radiology professionals in Saudi Arabia, identifying the educational and training needs to bridge knowledge gaps and enhance AI-related competencies.

Methods: This cross-sectional observational study surveyed radiology professionals across various hospitals in Saudi Arabia.

Secondary Sjögren's syndrome in a rheumatoid arthritis patient: A case report and review of literature.

Secondary Sjogren's syndrome (sSS) is a medical condition that occurs in individuals with autoimmune diseases such as systemic lupus erythematosus (SLE) and rheumatoid arthritis. It predominantly affects females rather than males. We present a case of a 32-year-old female with a 3-year history of rheumatoid arthritis (RA) who presented to the internal medicine and rheumatology clinic with several complaints, including swelling and tenderness in her left jaw, dry mouth (xerostomia), irritated eyes (xerophthalmia), severe joint pain, and a decreased in saliva production.

Evolving radiology continuing medical education: Tapping into the power of online learning.

Introduction: Global education, particularly Continuing Medical Education (CME) for healthcare professionals, is quickly shifting online. This study assesses the opportunities and challenges of adopting online learning in radiology CME. It explores how radiologists and radiographers have adapted to this digital shift and the changing landscape of radiology education.

Enhancing radiation safety awareness and practices among female radiographers: a comprehensive approach.

Objective: This study evaluates the level of radiation safety awareness and adherence to protective practices among pregnant female radiographers in the United Arab Emirates, aiming to identify gaps and develop targeted interventions for enhancing occupational safety.

Methods: Employing a cross-sectional design, the study surveyed 133 female radiographers using a self-developed questionnaire covering demographics, awareness and knowledge, workplace practices, communication, and satisfaction.

Results: The survey showed high awareness among radiographers, with 97% acknowledging radiation risks during pregnancy, although 42.

Deep learning-based automated bone age estimation for Saudi patients on hand radiograph images: a retrospective study.

Purpose: In pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeutic intervention planning. Conventional techniques for determining bone age depend on radiologists' subjective judgments, which may lead to non-negligible differences in the estimated bone age. This study proposes a deep learning-based model utilizing a fully connected convolutional neural network(CNN) to predict bone age from left-hand radiographs.