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Explainable Artificial Intelligence in Radiological Cardiovascular Imaging-A Systematic Review.

Matteo Haupt1, Martin H Maurer1, Rohit Philip Thomas1

  • 1Department of Diagnostic and Interventional Radiology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany.

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Summary
This summary is machine-generated.

Explainable Artificial Intelligence (XAI) enhances trust in AI for cardiovascular imaging by providing insights into deep learning models. Current research shows XAI methods like Grad-CAM are used, but quantitative evaluation and standardization are needed for clinical integration.

Keywords:
Grad-CAMcardiac CTcardiac MRIcardiovascular imagingdeep learningechocardiographyexplainable artificial intelligencemodel interpretability

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence
  • Explainable AI

Background:

  • Deep learning models in cardiovascular imaging face trust issues due to their "black box" nature.
  • Explainable Artificial Intelligence (XAI) aims to increase transparency and clinical integration of these AI models.
  • This review systematically analyzes XAI applications in radiological cardiovascular imaging.

Purpose of the Study:

  • To synthesize current research on the application of XAI methods in cardiovascular imaging.
  • To identify commonly used XAI techniques and cardiovascular imaging modalities.
  • To assess the clinical applications and limitations of XAI in this field.

Main Methods:

  • Systematic literature search across PubMed, Scopus, and Web of Science (Jan 2015-Mar 2025).
  • Inclusion of studies applying XAI techniques (e.g., Grad-CAM, SHAP, LIME) to cardiac CT, MRI, ultrasound, and CXR.
  • Exclusion of studies on nuclear medicine, non-imaging data, or lacking explainability features; PRISMA guidelines followed.

Main Results:

  • 28 studies met inclusion criteria, with ultrasound (9) and CT (9) being most common modalities.
  • XAI applied to disease classification (e.g., coronary artery disease) and abnormality detection.
  • Grad-CAM was the most frequent XAI method, primarily generating visual explanations.

Conclusions:

  • XAI shows significant potential for improving transparency and clinical acceptance of AI in cardiovascular imaging.
  • Current XAI evaluation is largely qualitative, lacking standardization.
  • Future research needs robust quantitative assessment, clinical validation, and advanced XAI techniques beyond saliency maps.