Publicação
Using artificial intelligence to prioritize pathology samples: report of a test drive
| dc.contributor.author | Rienda, Iván | |
| dc.contributor.author | Vale, João | |
| dc.contributor.author | Pinto, João | |
| dc.contributor.author | Polónia, António | |
| dc.contributor.author | Eloy, Catarina | |
| dc.date.accessioned | 2026-01-15T14:22:55Z | |
| dc.date.available | 2026-01-15T14:22:55Z | |
| dc.date.issued | 2025 | |
| dc.description.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. | eng |
| dc.identifier.citation | 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 | |
| dc.identifier.doi | 10.1007/s00428-024-03988-1 | |
| dc.identifier.eissn | 1432-2307 | |
| dc.identifier.issn | 0945-6317 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/31532 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Springer Nature | |
| dc.relation.hasversion | https://link.springer.com/article/10.1007/s00428-024-03988-1 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Computational pathology | |
| dc.subject | Cancer diagnosis | |
| dc.subject | Artificial intelligence | |
| dc.subject | Workflow | |
| dc.subject | Efficiency | |
| dc.subject | Foundation model | |
| dc.title | Using artificial intelligence to prioritize pathology samples: report of a test drive | eng |
| dc.type | other type of report | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 208 | |
| oaire.citation.startPage | 203 | |
| oaire.citation.title | Virchows Archiv | |
| oaire.citation.volume | 487 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |