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Related Experiment Video

Updated: Jan 20, 2026

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

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Published on: July 11, 2025

814

Next generation pathology: artificial intelligence enhances histopathology practice.

Balazs Acs1, Johan Hartman1

  • 1Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden.

The Journal of Pathology
|August 30, 2019
PubMed
Summary

Deep learning shows promise in tumor risk stratification using digital pathology. Further steps are needed to integrate artificial intelligence (AI) into routine histopathology for clinical utility.

Keywords:
artificial intelligencedeep learningstandardization

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Area of Science:

  • Pathology
  • Computational pathology
  • Digital pathology

Background:

  • Deep learning (AI) demonstrates potential in tumor risk stratification.
  • Clinical utility of AI in histopathology requires further validation.
  • A recent perspective outlines a roadmap for AI integration in histopathology.

Purpose of the Study:

  • To contextualize the roadmap for AI in histopathology with recent AI and digital image analysis findings.
  • To discuss the progression of AI from research to routine clinical application in pathology.

Main Methods:

  • Commentary on a perspective manuscript regarding AI in histopathology.
  • Review of recent findings in AI and digital image analysis for pathology.

Main Results:

  • AI holds significant promise for enhancing tumor risk stratification in pathology.
  • Bridging the gap between AI research and clinical histopathology practice is crucial.
  • Digital image analysis is key to advancing AI applications in pathology.

Conclusions:

  • AI integration into routine histopathology requires careful planning and validation.
  • The presented roadmap offers a framework for AI adoption in clinical practice.
  • Continued research in AI and digital pathology will drive clinical utility.