Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Advancing homebrew <scp>AI</scp> in diagnostic practice: opportunities and barriers <sup>†</sup>
0
Zitationen
2
Autoren
2025
Jahr
Abstract
In a recent issue of The Journal of Pathology, Calderaro et al present a timely and persuasive argument advocating for the integration of homebrew artificial intelligence (AI) models in diagnostic pathology. Their article is a robust defense of local model development within pathology departments as a pathway to democratizing digital diagnostics. This commentary expands on their premise, critically examining the real-world implications, practical limitations, and unmet needs of practicing pathologists. The commentary outlines both the opportunities and challenges for the widespread adoption of homebrew AI in pathology practice. Without institutional backing, digital infrastructure, and sustained training efforts, the promise of homebrew AI may falter. © 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.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.500 Zit.
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.129 Zit.
A survey on Image Data Augmentation for Deep Learning
2019 · 11.731 Zit.
QuPath: Open source software for digital pathology image analysis
2017 · 8.101 Zit.
Radiomics: Images Are More than Pictures, They Are Data
2015 · 7.981 Zit.