ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY >

2025 , Vol. 9 >Issue 4: 409 - 425

DOI: https://doi.org/10.26599/AUDT.2025.250089

Explainable Artificial Intelligence in Echocardiography

  • Hu Xuelin ,
  • Zhu Ye ,
  • Zhang Zisang ,
  • Quan Yuanting ,
  • Chen Wenwen ,
  • Chen Leichong ,
  • Xu Guangyu ,
  • Qin Luning ,
  • Xie Mingxing ,
  • Zhang Li
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  • aDepartment of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
    bClinical Research Center for Medical Imaging in Hubei Province
    cHubei Province Key Laboratory of Molecular Imaging
1Xuelin Hu and Ye Zhu contributed equally to this work.
Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, China (Mingxing Xie, Li Zhang).e-mail: xiemx@hust.edu.cn (MX X);
zli429@hust.edu.cn(L Z)

Received date: 2025-09-20

  Revised date: 2025-10-02

  Accepted date: 2025-10-13

  Online published: 2025-11-06

Copyright

2576-2508/© AUDT 2025
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Abstract

Recent advancements in artificial intelligence (AI) have generated novel opportunities and challenges in ultrasound imaging. Deep learning algorithms exhibit significant potential in analyzing echocardiographic images, encompassing tasks such as view classification, quantification of cardiac function, and the diagnosis and risk assessment of cardiac diseases. The “black box” nature of AI models limits their clinical applications. Adopting explainable artificial intelligence (XAI) methods is crucial for improving the transparency and understanding of model predictions. This paper reviews the progress of AI applications in echocardiography, with a particular emphasis on XAI as a technical solution to enhance the transparency of model decision-making and its benefits compared to traditional AI models. This review outlines recent advancements in XAI applications for echocardiography and their clinical implications.

Key words： Explainable artificial intelligence; Echocardiography; Deep learning

Cite this article

Hu Xuelin , Zhu Ye , Zhang Zisang , Quan Yuanting , Chen Wenwen , Chen Leichong , Xu Guangyu , Qin Luning , Xie Mingxing , Zhang Li . Explainable Artificial Intelligence in Echocardiography[J]. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY, 2025 , 9(4) : 409 -425 . DOI: 10.26599/AUDT.2025.250089

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