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[The Digital Lab: Practical digital workflow and integration of AI for routine in pathology, through the example of prostate cancer].

Annales de pathologie 2026

Kammerer-Jacquet SF, Allaume P, Guiheux K, Desdoigts S, Despax C, Pichon C, Brishoual A, Bednarski R, Baly O, Cavalcante R, Kletz F, Rioux-Leclercq N

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Digital pathology is a major technological revolution for pathology.

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BibTeX ↓ RIS ↓
APA Kammerer-Jacquet SF, Allaume P, et al. (2026). [The Digital Lab: Practical digital workflow and integration of AI for routine in pathology, through the example of prostate cancer].. Annales de pathologie. https://doi.org/10.1016/j.annpat.2026.01.011
MLA Kammerer-Jacquet SF, et al.. "[The Digital Lab: Practical digital workflow and integration of AI for routine in pathology, through the example of prostate cancer].." Annales de pathologie, 2026.
PMID 41688286

Abstract

Digital pathology is a major technological revolution for pathology. It modernizes routine practices and paves the way for the integration of artificial intelligence (AI) solutions for diagnostic and research purposes. At Rennes University Hospital, digital pathology has been routinely deployed since 2020, and an AI solution for the detection of prostate adenocarcinoma (Galen® Prostate, Ibex) has been integrated since July 2023. In this article, we review our experience in Rennes and assess both the impact of digitization on the various professions within the department and the prospective use of AI for routine diagnosis. The concordance between AI and pathologists was 93.2% for the detection of high-probability cancer and 99% for low-probability slides. Among slides with intermediate probability (43% of the total), cancer was confirmed in 4.7% of cases. For Gleason grading, the concordance rate was 76.6%. To date, the integration of AI has not changed the use of immunohistochemistry. A 10% failure rate related to pre-analytical artifacts was observed and is an area for improvement in our practices. Thus, the effectiveness of digital pathology and the use of AI models are closely dependent on pre-analytical quality and its organizational integration.