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Area of Science:

  • Medical imaging
  • Artificial intelligence
  • Radiology

Background:

  • Deep learning (DL) models demonstrate high performance in radiologic image analysis.
  • The reliability of DL model predictions is often limited by uncalibrated probabilities.
  • Trustworthy AI in healthcare requires quantifying the uncertainty of DL model outputs.

Purpose of the Study:

  • To review recent trends in uncertainty quantification (UQ) for deep learning (DL) in radiologic image analysis.
  • To provide a conceptual framework for understanding UQ in this domain.
  • To discuss applications, challenges, and future directions of UQ in medical DL.

Main Methods:

  • Literature review of UQ methods applied to DL in radiologic image analysis.
  • Conceptual analysis of UQ's role in enhancing DL model trustworthiness.
  • Discussion of practical implementation aspects and future research avenues.

Main Results:

  • UQ methods are crucial for assessing the validity of DL model predictions in radiology.
  • Implementing UQ alerts users to low-confidence predictions, enabling expert review.
  • UQ enhances the trustworthiness and clinical utility of DL tools in medical imaging.

Conclusions:

  • Uncertainty quantification is essential for the safe and effective deployment of deep learning in radiology.
  • Further research into UQ methods will improve the reliability and clinical adoption of AI-powered diagnostic tools.
  • Addressing challenges in UQ will unlock the full potential of AI in medical image analysis.