The case for homebrew <scp>AI</scp> in diagnostic pathology

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.

Authors

Institutions

Publication Details

Journal
The Journal of Pathology
Published
2025-07-04
DOI
https://doi.org/10.1002/path.6438
Citations
7
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
17.37

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The case for homebrew AI in diagnostic pathology

Helen Morement, Julien Caldéraro, Jakob Nikolas Kather, Stephen Gilbert et al.
7 citations
The Journal of Pathology
AI in cancer detection
17.37
article

The case for homebrew AI in diagnostic pathology

Helen Morement, Julien Caldéraro, Jakob Nikolas Kather, Stephen Gilbert, Frédérique Penault‐Llorca
article en
7 citations

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.

The Journal of PathologyVol. 266(4-5)
Inserm (FR), Université Paris-Est Créteil (FR), Université Clermont Auvergne (FR), Heidelberg University (DE), University Hospital Heidelberg (DE), Centre Hospitalier Universitaire Henri-Mondor (FR), Assistance Publique – Hôpitaux de Paris (FR), Fresenius (Germany) (DE), National Center for Tumor Diseases (DE), Centre Jean Perrin (FR), Institut Mondor de Recherche Biomédicale (FR), University Hospital Carl Gustav Carus (DE), Imagerie Moléculaire et Stratégies Théranostiques, Technische Universität Dresden (DE)
Pfizer, AstraZeneca, European Commission, Directorate-General for Health and Consumers, Daiichi Sankyo Europe
Openalex Percentile: Top 1%
AI in cancer detection
17.37
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.