The case for homebrew AI in diagnostic pathology

Fuente: Wiley Open Access
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Autori principali: Julien Calderaro, Helen Morement, Frédérique Penault‐Llorca, Stephen Gilbert, Jakob Nikolas Kather
Natura: Artículo Open Access
Pubblicazione: Wiley 2025
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author Julien Calderaro
Helen Morement
Frédérique Penault‐Llorca
Stephen Gilbert
Jakob Nikolas Kather
author_facet Julien Calderaro
Helen Morement
Frédérique Penault‐Llorca
Stephen Gilbert
Jakob Nikolas Kather
Julien Calderaro
Helen Morement
Frédérique Penault‐Llorca
Stephen Gilbert
Jakob Nikolas Kather
collection Wiley Open Access
contents The case for homebrew AI in diagnostic pathology Julien Calderaro Helen Morement Frédérique Penault‐Llorca Stephen Gilbert Jakob Nikolas Kather The Journal of Pathology AbstractArtificial 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. 10.1002/path.6438 http://creativecommons.org/licenses/by/4.0/
doi_str_mv 10.1002/path.6438
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spellingShingle The case for homebrew AI in diagnostic pathology
Julien Calderaro
Helen Morement
Frédérique Penault‐Llorca
Stephen Gilbert
Jakob Nikolas Kather
The Journal of Pathology
The case for homebrew AI in diagnostic pathology Julien Calderaro Helen Morement Frédérique Penault‐Llorca Stephen Gilbert Jakob Nikolas Kather The Journal of Pathology AbstractArtificial 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. 10.1002/path.6438 http://creativecommons.org/licenses/by/4.0/
title The case for homebrew AI in diagnostic pathology
topic The Journal of Pathology
url https://pathsocjournals.onlinelibrary.wiley.com/doi/10.1002/path.6438