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Using artificial intelligence to prioritize pathology samples: report of a test drive
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Computational pathology Cancer diagnosis Artificial intelligence Workflow Efficiency Foundation model
Contexto Educativo
Citação
Rienda, I., Vale, J., Pinto, J., Polónia, A., & Eloy, C. (2025). Using artificial intelligence to prioritize pathology samples: Report of a test drive. Virchows Archiv, 487(1), 203–208. https://doi.org/10.1007/s00428-024-03988-1
Editora
Springer Nature