The case for homebrew AI in diagnostic pathology
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
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.
Details
| Original language | English |
|---|---|
| Pages (from-to) | 390-394 |
| Number of pages | 5 |
| Journal | Journal of pathology |
| Volume | 266 |
| Issue number | 4-5 |
| Publication status | Published - 4 Jul 2025 |
| Peer-reviewed | Yes |
External IDs
| PubMed | 40613320 |
|---|---|
| ORCID | /0000-0002-1997-1689/work/188859756 |
| ORCID | /0000-0002-3730-5348/work/198594673 |
Keywords
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- artificial intelligence, deep learning, homebrew, laboratory-derived test, pathology, whole-slide image