ReviewInternational journal of laboratory hematology2026
Hematopathology Practice in the Digital Era: What has Changed?
Review in International journal of laboratory hematology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Whole-slide imaging in hematopathology: Current state, challenges and future opportunities.Journal of pathology informatics · 2026Review
- Hematopathology Practice in the Digital Era: What has Changed?International journal of laboratory hematology · 2026Review
- Deep Learning Identifies Abnormal Promyelocytes in Peripheral Blood Based on Morphological Analysis.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.