Evidence map›Paper›PMID 40698984›Full record

ReviewInternational journal of laboratory hematology2026

Digital Pathology in Hematopathology: From Vision to Deployment.

Ryan C Shean, Anton V Rets

Abstract readReview
In one paragraph

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ryan C SheanDepartment of Pathology, University of Utah, Salt Lake City, Utah, USA.
Anton V RetsDepartment of Pathology, University of Utah, Salt Lake City, Utah, USA.ORCID https://orcid.org/0000-0002-2148-162X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital pathology (DP) has evolved alongside other technical advances, transforming our daily lives and diagnostic medicine. It is likely that, as in other areas of life, science, and medicine, the overall level of digitization will continue to rise, along with an increasing number of groups implementing DP. This review explores the clinical DP ecosystem with a focus on hematology and hematopathology, addressing the benefits of DP, such as improved workflow efficiency, remote practice, enhanced collaboration, and integration of artificial intelligence tools. Several challenges and pitfalls are also highlighted, such as technical scanner challenges, image management system issues, IT infrastructure, regulations, and the critical (and expensive) topic of data storage, and retrieval for DP. We also propose a roadmap for the successful implementation of DP, designed to support institutions of all sizes to make the transition to DP. This roadmap emphasizes well-thought-out, strategic planning and aims to ensure that organizations and individuals considering a switch to DP are able to deliver meaningful benefits to pathologists, health systems, providers, and most importantly, patients.

Indexed as

Hematologic DiseasesHematologyImage Processing, Computer-AssistedPathology, ClinicalArtificial IntelligenceHumansdigital pathologyimage management systemwhole slide imaging

Identifiers

PMID40698984
PMCPMC13155282

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.