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Evaluating Explainability: A Framework for Systematic Assessment of Explainable AI Features in Medical Imaging.

Miguel A Lago1, Ghada Zamzmi1, Brandon Eich1

  • 1Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.

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Summary

We developed a framework to evaluate Artificial Intelligence (AI) explainability in medical imaging. This system quantifies explanation quality using consistency, plausibility, fidelity, and usefulness criteria for better AI-assisted diagnostics.

Keywords:
artificial intelligenceexplainabilityheatmapsinterpretabilitymedical imagingtransparency

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Explainable AI

Background:

  • Explainability features in AI devices offer insights into internal mechanisms.
  • Current evaluation techniques for AI explanations are lacking, especially in medical imaging.
  • A need exists for robust methods to assess the quality of AI-generated explanations in healthcare.

Purpose of the Study:

  • To propose a comprehensive framework for assessing and reporting explainable AI (XAI) features in medical images.
  • To establish quantifiable criteria for evaluating the quality of explanations provided by AI in medical devices.
  • To develop a scorecard for XAI methods in medical imaging to accompany AI devices.

Main Methods:

  • Developed an evaluation framework based on four criteria: consistency, plausibility, fidelity, and usefulness.
  • Defined consistency as the variability of explanations for similar inputs.
  • Defined plausibility as the closeness of the explanation to ground truth, fidelity as the alignment with model mechanisms, and usefulness as the impact on task performance.

Main Results:

  • The framework provides a quantitative method to assess AI explanation quality in medical imaging.
  • A scorecard was developed for a complete description and evaluation of XAI methods.
  • Case study using Ablation CAM and Eigen CAM illustrated heatmap evaluation for breast lesion detection.

Conclusions:

  • The proposed framework establishes criteria for quantifying the quality of explanations from medical AI devices.
  • This work addresses the lack of evaluation techniques for XAI in medical imaging.
  • The developed scorecard and criteria facilitate a thorough assessment of AI explainability in clinical applications.