TY - JOUR
T1 - WFUMB Commentary Paper on Artificial intelligence in Medical Ultrasound Imaging
AU - Cui, Xin Wu
AU - Goudie, Adrian
AU - Blaivas, Michael
AU - Chai, Young Jun
AU - Chammas, Maria Cristina
AU - Dong, Yi
AU - Stewart, Jonathon
AU - Jiang, Tian-An
AU - Liang, Ping
AU - Sehgal, Chandra M
AU - Wu, Xing-Long
AU - Hsieh, Peter Ching-Chang
AU - Adrian, Saftoiu
AU - Dietrich, Christoph F
N1 - Copyright © 2024 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
U2 - 10.1016/j.ultrasmedbio.2024.10.016
DO - 10.1016/j.ultrasmedbio.2024.10.016
M3 - Review article
C2 - 39672681
SN - 0301-5629
VL - 51
SP - 428
EP - 438
JO - Ultrasound in Medicine & Biology
JF - Ultrasound in Medicine & Biology
IS - 3
ER -