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Decoding Uncertainty Quantification for Oncology-An Illustration Using Radiomics.

Florian van Daalen1, Balu Krishna Sasidharan2, C Praveenraj2

  • 1Department of Health Promotion, Care and Public Health Research Institute (CAPHRI), Maastricht University, 6211 LK Maastricht, The Netherlands.

Diagnostics (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces uncertainty quantification (UQ) for artificial intelligence (AI) models in oncology. UQ helps clinicians understand AI prediction reliability, improving diagnostic confidence for radiologists and oncologists.

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Artificial intelligence (AI) models in oncology often prioritize accuracy over certainty communication.
Keywords:
aleatoricepistemicradiomicsthymic epithelial tumoursuncertainty quantification

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  • Lack of certainty assessment in AI predictions hinders clinical decision-making for oncologists and radiologists.