Artificial Intelligence in Abdominal, Gynecological, Obstetric, Musculoskeletal, Vascular and Interventional Ultrasound

Artificial Intelligence (AI) is a theoretical framework and systematic development of computational models designed to execute tasks that traditionally require human cognition. In medical imaging, AI is used for various modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and pathologies across multiple organ systems. However, integrating AI into medical ultrasound presents unique challenges compared to modalities like CT and MRI due to its operator-dependent nature and inherent variability in the image acquisition process. AI application to ultrasound holds the potential to mitigate multiple variabilities, recalibrate interpretative consistency, and uncover diagnostic patterns that may be difficult for humans to detect. Progress has led to significant innovation in medical ultrasound-based AI applications, facilitating their adoption in various clinical settings and for multiple diseases. This manuscript primarily aims to provide a concise yet comprehensive exploration of current and emerging AI applications in medical ultrasound within abdominal, musculoskeletal, and obstetric & gynecological and interventional medical ultrasound. The secondary aim is to discuss present limitations and potential challenges such technological implementations may encounter.