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Beyond Post hoc Explanations: A Comprehensive Framework for Accountable AI in Medical Imaging Through Transparency,

Yashbir Singh1, Quincy A Hathaway2, Varekan Keishing1

  • 1Radiology, Mayo Clinic, Rochester, MN 55905, USA.

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Summary
This summary is machine-generated.

Explainable AI (XAI) methods like LIME show higher fidelity in medical imaging than SHAP or Grad-CAM. However, post hoc explanations lack stability, necessitating a new accountability framework for trustworthy AI in healthcare.

Keywords:
accountabilityartificial intelligenceclinical decision-makingexplainable AImedical imagingmeta-analysissystematic review

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Explainable AI

Background:

  • Deep learning in medical imaging offers diagnostic advances but faces clinical adoption barriers due to its "black-box" nature.
  • Current explainable AI (XAI) methods often provide post hoc explanations, potentially misrepresenting AI model confidence and impacting clinical decisions.

Purpose of the Study:

  • To systematically review and meta-analyze the fidelity, stability, and performance trade-offs of XAI methods across medical imaging modalities.
  • To evaluate the impact of noise perturbation on XAI stability in radiology, pathology, and ophthalmology.
  • To propose an accountability framework for developing and deploying trustworthy AI in clinical settings.

Main Methods:

  • Systematic review and meta-analysis of 67 studies evaluating XAI in 23 radiology, 19 pathology, and 25 ophthalmology applications.
  • Analysis of 847 identified studies to compare the fidelity of LIME, SHAP, and Grad-CAM.
  • Assessment of XAI stability under noise perturbation across different medical imaging modalities.

Main Results:

  • LIME demonstrated superior fidelity (0.81) compared to SHAP (0.38) and Grad-CAM (0.54) across all modalities.
  • Post hoc explanations exhibited poor stability under noise; SHAP degraded by 53% in ophthalmology versus 11% in radiology.
  • Interpretable models incurred a consistent 5-7% AUC performance penalty, with modality-specific stability patterns observed.

Conclusions:

  • Tailored XAI approaches are essential due to modality-specific stability patterns.
  • A three-pillar accountability framework is proposed, emphasizing transparency, interpretable design, and cautious deployment of post hoc explanations with uncertainty quantification.
  • This framework aims to enhance clinical decision-making quality and patient safety by fostering genuinely accountable AI systems.