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This study introduces an interactive framework to enhance clinician trust in Convolutional Neural Networks (CNNs) for medical X-ray analysis. It makes AI decisions interpretable by translating CNN layers into symbolic rules, allowing expert intervention.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Machine Learning Interpretability
  • Clinical Decision Support Systems

Background:

  • Deep Learning models, particularly Convolutional Neural Networks (CNNs), excel at extracting complex features from medical X-ray images.
  • Limited interpretability of CNNs hinders their adoption in clinical practice due to a lack of clinician trust.
  • Bridging the gap between AI capabilities and clinical acceptance requires interpretable AI solutions.

Purpose of the Study:

  • To develop an interactive framework to increase clinician trust in CNN-based medical image analysis.
  • To enable clinicians to question and intervene in CNN decision-making processes.
  • To create an interpretable refinement of data-driven CNNs aligned with medical best practices.

Main Methods:

  • Translating a layer of a trained CNN into a compact set of symbolic rules.
  • Utilizing expert interactions with rule visualizations to promote clinically-relevant CNN kernel usage.
  • Employing radiomics analyses and permutation evaluations to define and validate kernel relevance.
  • Removing clinically non-meaningful CNN kernels without compromising model performance.

Main Results:

  • The framework successfully translates CNN layers into interpretable symbolic rules.
  • Clinician interaction with rule visualizations identified and promoted clinically-relevant CNN kernels.
  • Removal of non-meaningful kernels did not negatively impact overall model performance.
  • The approach yielded an interpretable CNN refinement aligned with clinical expertise.

Conclusions:

  • The proposed interactive framework enhances clinician trust by making CNN decisions transparent and controllable.
  • Symbolic rule translation and expert-guided kernel refinement are effective strategies for interpretable AI in radiology.
  • This method facilitates the integration of AI tools into clinical workflows by ensuring alignment with medical best practices.