Artificial Intelligence in Ultrasound Imaging

Ultrasound imaging is widely used across cardiology, hepatology, obstetrics, breast and thyroid imaging, and emergency care because it is real-time, noninvasive, and relatively accessible. However, its diagnostic performance remains influenced by operator experience, image quality, scanner settings, and interpretation variability. Artificial intelligence (AI) has emerged as a promising support tool for ultrasound, assisting with image acquisition, quality assessment, view classification, segmentation, measurement, lesion characterization, and structured reporting. This narrative review summarizes recent developments in AI-assisted ultrasound imaging, emphasizing its technical foundations, clinical applications, validation challenges, and relevance to Gulf health care systems. Current evidence suggests that AI may improve workflow efficiency, reduce interobserver variability, and support diagnostic decision-making in selected ultrasound tasks, particularly when models are trained and tested on large, diverse data sets. Nevertheless, the clinical readiness of many AI tools remains constrained by retrospective study designs, single-center data sets, limited external validation, vendor-dependent image variability, and insufficient prospective evaluation. These limitations are particularly salient in Gulf health care, where ultrasound services are delivered across heterogeneous public, private, military, and academic institutions that use different equipment, workflows, and operator training backgrounds. Gulf countries are well-positioned to adopt AI-enabled ultrasound because of the ongoing digital health transformation and emerging regulatory frameworks, including the Saudi SFDA guidance and the UAE AI governance initiatives. However, responsible implementation will require region-specific, multicenter, multivendor validation, transparent reporting of model performance and failure cases, clinician training, and privacy-preserving data governance. AI should therefore be viewed not as a replacement for ultrasound professionals but as a decision-support technology that may improve consistency, efficiency, and diagnostic confidence when carefully validated in real-world clinical settings.