Evidence map›Paper›PMID 38189845›Full record

ReviewPathologie (Heidelberg, Germany)2024

[Digital transformation of a routine histopathology lab : Dos and don'ts!]

Viola Iwuajoku, Anette Haas, Kübra Ekici, Mohammad Zaid Khan, Fabian Stögbauer, Katja Steiger, Carolin Mogler, Peter J Schüffler

Abstract readEnglish AbstractReview
In one paragraph

Review in Pathologie (Heidelberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. [Artificial intelligence for pathology-how, where, and why?]Pathologie (Heidelberg, Germany) · 2024
    Article
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

8 authors.

Viola IwuajokuInstitut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland.
Anette HaasInstitut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland.
Kübra EkiciInstitut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland.
Mohammad Zaid KhanInstitut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland.
Fabian StögbauerInstitut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland.
Katja SteigerInstitut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland.
Carolin MoglerInstitut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland.
Peter J SchüfflerInstitut für Pathologie, TUM School of Medicine and Health, Technische Universität München, Trogerstraße 18, 81675, München, Deutschland. peter.schueffler@tum.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The implementation of digital histopathology in the laboratory marks a crucial milestone in the overall digital transformation of pathology. This shift offers a range of new possibilities, including access to extensive datasets for AI-assisted analyses, the flexibility of remote work and home office arrangements for specialists, and the expedited and simplified sharing of images and data for research, conferences, and tumor boards. However, the transition to a fully digital workflow involves significant technological and personnel-related efforts. It necessitates careful and adaptable change management to minimize disruptions, particularly in the personnel domain, and to prevent the loss of valuable potential from employees who may be resistant to change. This article consolidates our institute's experiences, highlighting technical and personnel-related challenges encountered during the transition to digital pathology. It also presents a comprehensive overview of potential difficulties at various interfaces when converting routine operations to a digital workflow.

Indexed as

Laboratories, ClinicalPathologyWorkflowArtificial intelligenceChange managementComputer-assisted image processingDigital pathologyWorkflow

Identifiers

PMID38189845
PMCPMC10902067

What OpenQuestion 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 OpenQuestion graph.