Evidence map›Paper›PMID 40613320›Full record

ArticleThe Journal of pathology2025

The case for homebrew AI in diagnostic pathology.

Julien Calderaro, Helen Morement, Frédérique Penault-Llorca, Stephen Gilbert, Jakob Nikolas Kather

Abstract read
In one paragraph

Article in The Journal of pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Terminally exhausted CD8Frontiers in immunology · 2025
    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

5 authors.

Julien CalderaroDépartement de Pathologie, Assistance Publique Hôpitaux de Paris, Groupe Hospitalier Henri Mondor, Créteil, France.
Helen MorementAMMF-The Cholangiocarcinoma Charity, Chelmsford, UK.
Frédérique Penault-LlorcaINSERM U1240, [Molecular Imaging & Theragnostic Strategies (IMOST)], University Clermont Auvergne, Clermont-Ferrand, France.
Stephen Gilbert *Else Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
Jakob Nikolas Kather *Else Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) methods for digital pathology have tremendous potential to improve cancer diagnostics, biomarkers, and ultimately patient care. These AI methods, if marketed and sold, require authorisation or clearance as in vitro diagnostic (IVD) devices by regulatory bodies like the Food and Drug Administration (FDA) in the USA or Notified Bodies in the European Union (EU). Many AI tools for digital pathology are unlikely to be commercially viable and taken up by commercial entities ready to navigate these complex and costly processes. However, a longstanding quality framework already exists that allows for lab-developed tests, colloquially known as 'homebrew' tests, that are locally validated and performed under the responsibility and oversight of the pathologist. Here we argue for advancing homebrew AI systems within this existing framework to enhance patients' access to supportive digital diagnostic tools. We outline how homebrew AI models are currently permitted under regulatory provisions in the USA and the European Union, how a new US FDA rule may effectively regulate them out of existence, and propose steps to facilitate the safe and effective integration of homebrew AI models in pathology practice. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Indexed as

Artificial IntelligenceImage Interpretation, Computer-AssistedPathology, ClinicalEuropean UnionHumansUnited StatesUnited States Food and Drug Administrationartificial intelligencedeep learninghomebrewlaboratory‐derived testpathologywhole‐slide image

Identifiers

PMID40613320
PMCPMC12256376

What OpenQuestion holds

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LicenceCC BY
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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.