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Related Experiment Video

Updated: Dec 18, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

661

Explainable AI (xAI) for Anatomic Pathology.

Akif B Tosun1, Filippo Pullara1, Michael J Becich1,2

  • 1SpIntellx Inc.

Advances in Anatomic Pathology
|June 17, 2020
PubMed
Summary
This summary is machine-generated.

Explainable AI (xAI) enhances trust in computational pathology by making artificial intelligence (AI) decisions transparent. This technology, demonstrated in HistoMapr-Breast, aids pathologists in diagnosing whole slide images (WSIs) more efficiently and accurately.

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Artificial intelligence in medicine

Background:

  • Whole slide images (WSIs) are increasingly adopted for pathology diagnosis following FDA approval.
  • Computational pathology, utilizing AI and machine learning on WSIs, offers potential for improved accuracy and efficiency.
  • Trust in AI is hindered by its black-box nature, and consensus on integrating AI into pathology workflows is lacking.

Purpose of the Study:

  • To introduce explainable AI (xAI) as a transparent alternative to black-box AI models in computational pathology.
  • To outline xAI-enabled applications for enhancing anatomic pathology workflows.
  • To present HistoMapr-Breast, an xAI software for breast core biopsies.

Main Methods:

  • Development of computational pathology systems incorporating explainable AI (xAI) mechanisms.
  • Application of xAI to analyze whole slide images (WSIs).
  • Creation of HistoMapr-Breast software for automated preview and region of interest recognition in breast core WSIs.

Main Results:

  • xAI systems can reveal the underlying causes of AI decisions, promoting safety and reliability.
  • HistoMapr-Breast automatically previews WSIs, identifies key diagnostic regions, and presents them interactively.
  • xAI applications improve the efficiency and accuracy of anatomic pathology practices.

Conclusions:

  • Explainable AI (xAI) addresses trust concerns associated with AI in pathology.
  • xAI-powered tools like HistoMapr-Breast can serve as interactive computational guides for pathologists.
  • The integration of xAI is anticipated to advance computer-assisted primary diagnosis in pathology.