Hematopathology Practice in the Digital Era: What has Changed?

Hematopathology workflows are complex, since they include numerous data points necessary for guiding further testing, diagnosis, and patient management. The workflows start with complete blood cell counts, with subsequent morphologic evaluation of peripheral blood (PB) and bone marrow (BM). Digital pathology has the potential to revolutionize PB and BM assessment through the implementation of artificial intelligence for assisted and automated evaluation, but there remain major hurdles toward this ultimate goal, such as lack of regulatory oversight, data standardization, insufficient knowledge and training, and resistance to change, among others. This article reviews the current state of digitalization in the hematopathology practice, recent research using machine learning models for automated specimen analysis, outlines the advantages and barriers facing clinical implementation of artificial intelligence, and offers prospective artificial intelligence-driven clinical workflows for efficient and comprehensive clinical workup.

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Publication Details

Journal
International Journal of Laboratory Hematology
Published
2025-08-14
DOI
https://doi.org/10.1111/ijlh.14515
Primary Topic
Medical Coding and Health Information
Type
article
Field-Weighted Citation Impact
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article

Hematopathology Practice in the Digital Era: What has Changed?

Olga Pozdnyakova
International Journal of Laboratory Hematology
Medical Coding and Health Information
article

Hematopathology Practice in the Digital Era: What has Changed?

Olga Pozdnyakova
article en

Abstract

Hematopathology workflows are complex, since they include numerous data points necessary for guiding further testing, diagnosis, and patient management. The workflows start with complete blood cell counts, with subsequent morphologic evaluation of peripheral blood (PB) and bone marrow (BM). Digital pathology has the potential to revolutionize PB and BM assessment through the implementation of artificial intelligence for assisted and automated evaluation, but there remain major hurdles toward this ultimate goal, such as lack of regulatory oversight, data standardization, insufficient knowledge and training, and resistance to change, among others. This article reviews the current state of digitalization in the hematopathology practice, recent research using machine learning models for automated specimen analysis, outlines the advantages and barriers facing clinical implementation of artificial intelligence, and offers prospective artificial intelligence-driven clinical workflows for efficient and comprehensive clinical workup.

International Journal of Laboratory Hematology
Hospital of the University of Pennsylvania (US)
Openalex Percentile: Top 77%
Medical Coding and Health Information
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