Artificial intelligence in pancreatic cancer histopathology and diagnostics - implications for clinical decisions and biomarker discovery?
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F25%3A00082165" target="_blank" >RIV/65269705:_____/25:00082165 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14110/25:00141397 RIV/00159816:_____/25:00082488
Result on the web
<a href="https://celldiv.biomedcentral.com/articles/10.1186/s13008-025-00158-w" target="_blank" >https://celldiv.biomedcentral.com/articles/10.1186/s13008-025-00158-w</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1186/s13008-025-00158-w" target="_blank" >10.1186/s13008-025-00158-w</a>
Alternative languages
Result language
angličtina
Original language name
Artificial intelligence in pancreatic cancer histopathology and diagnostics - implications for clinical decisions and biomarker discovery?
Original language description
Artificial intelligence (AI) and machine learning (ML) are rapidly advancing fields within computer science, driving significant progress in cancer diagnostics. Various ML models have been developed to assist diagnosis, guide therapy decisions, and facilitate early disease detection. In this review, we discuss diverse AI and ML approaches and critically evaluate their applications and limitations in pancreatic cancer histopathology, diagnostics, and biomarker discovery.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10601 - Cell biology
Result continuities
Project
<a href="/en/project/NU23-08-00241" target="_blank" >NU23-08-00241: Development of ex-vivo cellular models for pancreatic adenocarcinoma: markers and targets for precision medicine</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Cell Division
ISSN
1747-1028
e-ISSN
1747-1028
Volume of the periodical
20
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
10
Pages from-to
15
UT code for WoS article
001510482100001
EID of the result in the Scopus database
2-s2.0-105008215627