In CT and MRI, image acquisition is performed automatically with a specific patient, a fixed measurement time, and consistent image settings. However, US imaging exhibits characteristic issues relating to image quality control. It is worth considering the combination of US diagnostic capabilities and laboratory tests as the multi-biomarker strategy for prediction of clinical outcome. Current advances in image rendering technologies and the miniaturization of ultrasonic diagnostic equipment have led to its use in point-of-care testing in emergency medical care, palliative care, and home medical care. Therefore, US imaging is commonly used for screening as well as definitive diagnosis in numerous medical fields.

In contrast, computed tomography (CT) has a risk of radiation exposure, and magnetic resonance imaging (MRI) is non-invasive but costly and time-consuming. Ultrasound (US) imaging is superior to other medical imaging modalities in terms of its convenience, non-invasiveness, and real-time properties.

We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control.

However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Artificial intelligence (AI) is being increasingly adopted in medical research and applications.

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