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Digital Pathology and Artificial Intelligence in Breast Pathology.

Matthew G Hanna1

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Digital and computational pathology are transforming breast cancer diagnosis and management. Machine learning enhances accuracy and productivity in workflows, promising standardized care and novel insights.

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Area of Science:

  • Pathology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Digital and computational pathology are increasingly integrated into breast pathology.
  • Machine learning (ML) offers tools to enhance diagnostic accuracy, biomarker quantification, and outcome prediction.

Purpose of the Study:

  • To review the current applications and future potential of digital and computational pathology in breast pathology.
  • To discuss the benefits, challenges, and emerging solutions in this evolving field.

Main Methods:

  • Review of current literature and commercial/research applications in digital and computational breast pathology.
  • Analysis of use cases demonstrating improvements in accuracy, productivity, and discovery.

Main Results:

  • Digital and computational pathology tools show promise in diagnostic, predictive, and prognostic applications.
  • Identified challenges include pre-analytical variability, interoperability, and explainability.

Conclusions:

  • Digital and computational pathology hold significant potential for standardizing breast cancer diagnosis and improving patient management.
  • Emerging solutions and future directions aim to overcome current challenges and unlock novel insights.