WFUMB Commentary Paper on Artificial intelligence in Medical Ultrasound Imaging

Xin Wu Cui, Adrian Goudie, Michael Blaivas, Young Jun Chai, Maria Cristina Chammas, Yi Dong, Jonathon Stewart, Tian-An Jiang, Ping Liang, Chandra M Sehgal, Xing-Long Wu, Peter Ching-Chang Hsieh, Saftoiu Adrian, Christoph F Dietrich

Research output: Contribution to journalReview articlepeer-review

Abstract

Artificial intelligence (AI) is defined as the theory and development of computer systems able to perform tasks normally associated with human intelligence. At present, AI has been widely used in a variety of ultrasound tasks, including in point-of-care ultrasound, echocardiography, and various diseases of different organs. However, the characteristics of ultrasound, compared to other imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), poses significant additional challenges to AI. Application of AI can not only reduce variability during ultrasound image acquisition, but can standardize these interpretations and identify patterns that escape the human eye and brain. These advances have enabled greater innovations in ultrasound AI applications that can be applied to a variety of clinical settings and disease states. Therefore, The World Federation of Ultrasound in Medicine and Biology (WFUMB) is addressing the topic with a brief and practical overview of current and potential future AI applications in medical ultrasound, as well as discuss some current limitations and future challenges to AI implementation.

Original languageEnglish
Pages (from-to)428-438
Number of pages11
JournalUltrasound in Medicine & Biology
Volume51
Issue number3
Early online date2024
DOIs
Publication statusE-pub ahead of print - 2024

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