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Related Experiment Video

Updated: Sep 23, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Published on: November 30, 2022

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Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.

Bas H M van der Velden1, Hugo J Kuijf1, Kenneth G A Gilhuijs1

  • 1Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, Utrecht, GA 3508, the Netherlands.

Medical Image Analysis
|May 16, 2022
PubMed
Summary
This summary is machine-generated.

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Explainable artificial intelligence (XAI) is crucial for deep learning in medical imaging. This survey reviews XAI methods, offering a framework for classifying techniques and highlighting future opportunities in this vital field.

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Science

Background:

  • Deep learning methods are increasingly used in medical image analysis.
  • The need for explainability in AI decision-making is growing, particularly in high-stakes medical applications.
  • Explainable Artificial Intelligence (XAI) addresses this need by making AI models understandable.

Purpose of the Study:

  • To provide a comprehensive overview of XAI techniques applied to deep learning in medical image analysis.
  • To introduce a framework for classifying and evaluating XAI methods in this domain.
  • To identify current trends and future research directions for XAI in medical imaging.

Main Methods:

  • A systematic survey of research papers on XAI in medical image analysis was conducted.
Keywords:
Deep learningExplainable artificial intelligenceInterpretable deep learningMedical image analysisSurvey

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  • A novel framework based on XAI criteria was developed to categorize existing methods.
  • Papers were classified according to the proposed framework and the anatomical region analyzed.

    • Main Results:

    • The survey categorizes various XAI techniques used in conjunction with deep learning for medical imaging.
    • The proposed framework provides a structured approach to understanding the landscape of XAI in medical image analysis.
    • Key challenges and limitations of current XAI methods are identified.

    Conclusions:

    • XAI is essential for the trustworthy adoption of deep learning in medical image analysis.
    • Standardized frameworks are needed to evaluate and compare XAI techniques effectively.
    • Future research should focus on developing more robust, interpretable, and clinically applicable XAI solutions for medical imaging.