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Updated: May 5, 2026

Database-guided Flow-cytometry for Evaluation of Bone Marrow Myeloid Cell Maturation
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Hematopathology Practice in the Digital Era: What has Changed?

Olga Pozdnyakova1

  • 1Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, USA.

International Journal of Laboratory Hematology
|August 14, 2025
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Summary

Digital pathology and artificial intelligence (AI) can enhance hematopathology workflows for blood cell counts and tissue analysis. Challenges like regulation and data standardization must be addressed for AI implementation in clinical practice.

Keywords:
artificial intelligencebone marrowdigital workflowshematopathologyperipheral blood

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Area of Science:

  • Hematopathology
  • Digital Pathology
  • Artificial Intelligence

Background:

  • Hematopathology workflows involve complex data for diagnosis and patient management, starting with blood cell counts and morphologic evaluation of peripheral blood (PB) and bone marrow (BM).
  • Digital pathology offers potential for revolutionizing PB and BM assessment via AI-assisted and automated evaluation.

Purpose of the Study:

  • To review the current state of digitalization in hematopathology.
  • To discuss recent research utilizing machine learning for automated specimen analysis.
  • To outline the advantages and barriers to AI implementation and propose future AI-driven workflows.

Main Methods:

  • Review of current literature on digital pathology and AI in hematopathology.
  • Analysis of machine learning models for automated analysis of blood and bone marrow specimens.
  • Discussion of implementation challenges and prospective workflow development.

Main Results:

  • Digital pathology and AI show promise for improving hematopathology diagnostics.
  • Significant hurdles including regulatory oversight, data standardization, and workforce training impede widespread adoption.
  • AI-driven workflows could enhance efficiency and comprehensiveness in clinical workup.

Conclusions:

  • Digitalization and AI hold transformative potential for hematopathology practice.
  • Overcoming implementation barriers is crucial for realizing the benefits of AI in clinical settings.
  • Future workflows integrating AI can lead to more efficient and accurate patient management.