Evidence map›Paper›PMID 40528234›Full record

ReviewCell division2025

Artificial intelligence in pancreatic cancer histopathology and diagnostics - implications for clinical decisions and biomarker discovery?

Petra Weselá, Michal Eid, Petr Moravčík, Jakub Vlažný, Jan Hlavsa, Vladimír Procházka, Zdeněk Kala, Petr Vaňhara

Abstract readReview
In one paragraph

Review in Cell division, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Petra WeseláDepartment of Histology and Embryology, Faculty of Medicine, Masaryk University, Kamenice 3, Brno, 625 00, Czech Republic.
Michal EidDepartment of Internal Medicine, Hematology and Oncology, University Hospital Brno, Brno, Czech Republic.
Petr MoravčíkSurgery Clinic, University Hospital Brno, Brno, Czech Republic.
Jakub VlažnýCenter for Precision Medicine, University Hospital Brno, Brno, Czech Republic.
Jan HlavsaSurgery Clinic, University Hospital Brno, Brno, Czech Republic.
Vladimír ProcházkaSurgery Clinic, University Hospital Brno, Brno, Czech Republic.
Zdeněk KalaSurgery Clinic, University Hospital Brno, Brno, Czech Republic.
Petr VaňharaDepartment of Histology and Embryology, Faculty of Medicine, Masaryk University, Kamenice 3, Brno, 625 00, Czech Republic. pvanhara@med.muni.cz.

Funding

Czech Health Research Council NU23-08-00241Masaryk University MUNI/A/1558/2023Masaryk University MUNI/A/1738/2024
6 · The paper itself

Abstract

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.

Indexed as

Artificial intelligenceBiomarker discoveryMachine learningMultimodal learningPancreatic cancerPancreatic ductal adenocarcinoma

Identifiers

PMID40528234
PMCPMC12175320

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.