Deep Learning for Clinical Ultrasound Imaging: From Supervised Approaches to Foundation Models

Deep learning models have traditionally been developed and trained to perform specific tasks, which limits their generalizability across different domains. Recently, the field has experienced a paradigm shift toward foundation models, wherein AI systems are pre-trained on extensive datasets to support a broad range of tasks across multiple domains. This shift is particularly promising for ultrasound imaging, a relatively low-cost and versatile modality in clinical practice. However, their adoption remains limited due to challenges such as scarce labeled data, domain-specific variability, and the need for user expertise. This review first outlines the workflow and design principles of deep learning-based ultrasound clinical applications, with emphasis on advancements in supervised and self-supervised learning, discriminative models, generative AI models, foundation models, and other miscellaneous models. Recent literature on ultrasound image analysis is then reviewed in detail. Furthermore, deep learning-based ultrasound clinical applications are summarized, and the potential of foundation models to enhance diagnostic performance and expand the scope of ultrasound in clinical settings is highlighted. Finally, key challenges are discussed, and future directions for deep learning in clinical ultrasound are proposed.