ArticleVirchows Archiv : an international journal of pathology2025
Using artificial intelligence to prioritize pathology samples: report of a test drive.
Article in Virchows Archiv : an international journal of pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- An artificial intelligence solution for evaluation of prostate needle core biopsy specimens: impact in a non-specialist setting.Virchows Archiv : an international journal of pathology · 2026Article
- What practicing pathologists and oncologists should know about the new computational pathology-based companion diagnostic tools.The Journal of pathology · 2026Review
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Digital transformation of pathology - the European Society of Pathology expert opinion paper.Virchows Archiv : an international journal of pathology · 2025Article
- Computational pathology in the age of artificial intelligence - embrace not fear.The journal of pathology. Clinical research · 2025Review
- Multimodal integration strategies for clinical application in oncology.Frontiers in pharmacology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The digital transformation of pathology, through automation and computational tools, addresses current challenges in the field. This study evaluates Paige Pan Cancer, a novel artificial intelligence tool based on the Virchow foundation model, designed to flag invasive cancer in haematoxylin and eosin-stained slides from 16 primary tissue types. Using 62 cases from the Ipatimup Pathology Laboratory, we found the tool had a sensitivity of 93.3% and specificity of 87.5% in biopsies, and 94.7% sensitivity and 75.0% specificity in resections. Overall accuracy was 90.3%. Despite some misclassifications, Paige Pan Cancer demonstrates high sensitivity as a multi-organ screening tool in clinical practice.
Indexed as
Identifiers
39627613What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.