Artificial intelligence-based ultrasound imaging technologies for hepatic diseases

Application and prospects of AI-based radiomics in ultrasound diagnosis

Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.

Advances in deep learning: From diagnosis to treatment

Deep learning has brought about a revolution in the field of medical diagnosis and treatment. The use of deep learning in healthcare has grown exponentially in recent years, achieving physician-level accuracy in various diagnostic tasks and supporting applications such as electronic health records and clinical voice assistants. The emergence of medical foundation models, as a new approach to deep learning, has greatly improved the reasoning ability of machines. Characterized by large training datasets, context awareness, and multi-domain applications, medical foundation models can integrate various forms of medical data to provide user-friendly outputs based on a patien's information. Medical foundation models have the potential to integrate current diagnostic and treatment systems, providing the ability to understand multi-modal diagnostic information and real-time reasoning ability in complex surgical scenarios. Future research on foundation model-based deep learning methods will focus more on the collaboration between physicians and machines. On the one hand, developing new deep learning methods will reduce the repetitive labor of physicians and compensate for shortcomings in their diagnostic and treatment capabilities. On the other hand, physicians need to embrace new deep learning technologies, comprehend the principles and technical risks of deep learning methods, and master the procedures for integrating them into clinical practice. Ultimately, the integration of artificial intelligence analysis with human decision-making will facilitate accurate personalized medical care and enhance the efficiency of physicians.