J Korean Soc Radiol.  2025 Mar;86(2):216-226. 10.3348/jksr.2025.0019.

Clinical Application of Artificial Intelligence in Breast Ultrasound

Affiliations
  • 1BeamWorks Inc., Daegu, Korea
  • 2School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea
  • 3Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea
  • 4Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
  • 5Department of Surgery, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea
  • 6Municipal State Enterprise on the Right of Economic Management “Almaty Oncology Center,” Public Health Department of Kazakhstan, Almaty, Kazakhstan
  • 7JSC “Kazakh Research Institute of Oncology and Radiology,” Almaty, Kazakhstan
  • 8Nazarbayev University, Astana, Kazakhstan

Abstract

Breast cancer is the most common cancer in women worldwide, and its early detection is critical for improving survival outcomes. As a diagnostic and screening tool, mammography can be less effective owing to the masking effect of fibroglandular tissue, but breast US has good sensitivity even in dense breasts. However, breast US is highly operator dependent, highlighting the need for artificial intelligence (AI)-driven solutions. Unlike other modalities, US is performed using a handheld device that produces a continuous real-time video stream, yielding 12000–48000 frames per examination. This can be significantly challenging for AI development and requires real-time AI inference capabilities. In this review, we classified AI solutions as computer-aided diagnosis and computer-aided detection to facilitate a functional understanding and review commercial software supported by clinical evidence. In addition, to bridge healthcare gaps and enhance patient outcomes in geographically under resourced areas, we propose a novel framework by reviewing the existing AI-based triage workflows including mobile ultrasound.

Keyword

Artificial Intelligence; Breast Neoplasm; Ultrasonography; Breast Diseases
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