Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system

Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system

Abstract Background Digital pathology (DP) has revolutionized cancer diagnostics and enabled the development of deep-learning (DL) models aimed at supporting pathologists in their daily work and improving patient care. However, the clinical adoption of such models remains challenging. Here, we descr...

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Bibliographic Details
Main Authors: Miriam Angeloni, Davide Rizzi, Simon Schoen, Alessandro Caputo, Francesco Merolla, Arndt Hartmann, Fulvia Ferrazzi, Filippo Fraggetta
Format: Article
Language:English
Published: BMC 2025-05-01
Series:Genome Medicine
Subjects:
Anatomic pathology laboratory information system
Computational pathology
Deep-learning models
Digital pathology
HL7
Integration
Online Access:https://doi.org/10.1186/s13073-025-01484-y
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https://doi.org/10.1186/s13073-025-01484-y

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