Evidence map›Paper›PMID 39941923›Full record

ReviewCancers2025

Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.

Amin Naemi, Ashkan Tashk, Amir Sorayaie Azar, Tahereh Samimi, Ghanbar Tavassoli, Anita Bagherzadeh Mohasefi, Elaheh Nasiri Khanshan, Mehrdad Heshmat Najafabad, Vafa Tarighi, Uffe Kock Wiil and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. 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

13 authors.

Amin NaemiNordcee, Department of Biology, University of Southern Denmark, 5230 Odense, Denmark.ORCID 0000-0003-4501-4310
Ashkan TashkCognitive Systems, DTU Compute, The Technical University of Denmark (DTU), 2800 Copenhagen, Denmark.
Amir Sorayaie AzarSDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark.ORCID 0000-0001-7797-7790
Tahereh SamimiStudent Research Committee, Urmia University of Medical Sciences, Urmia 1138, Iran.
Ghanbar TavassoliDepartment of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 969, Iran.ORCID 0000-0001-7600-7346
Anita Bagherzadeh MohasefiDepartment of Computer Engineering, Urmia University, Urmia 165, Iran.
Elaheh Nasiri KhanshanDepartment of Computer Engineering, Urmia University, Urmia 165, Iran.
Mehrdad Heshmat NajafabadDepartment of Computer Engineering, Urmia University, Urmia 165, Iran.ORCID 0009-0001-6282-6081
Vafa TarighiDepartment of Computer Engineering, Urmia University, Urmia 165, Iran.
Uffe Kock WiilSDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark.ORCID 0000-0001-6898-4083
Jamshid Bagherzadeh MohasefiSDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark.ORCID 0000-0003-2497-0186
Habibollah PirnejadPatient Safety Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia 1138, Iran.ORCID 0000-0001-9284-5702
Zahra NiazkhaniNephrology and Kidney Transplant Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia 1138, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesThis systematic literature review examines the application of Artificial Intelligence (AI) in the diagnosis, treatment, and follow-up of metastatic gastrointestinal cancers.

methodsThe databases PubMed, Scopus, Embase (Ovid), and Google Scholar were searched for published articles in English from January 2010 to January 2022, focusing on AI models in metastatic gastrointestinal cancers.

resultsforty-six studies were included in the final set of reviewed papers. The critical appraisal and data extraction followed the checklist for systematic reviews of prediction modeling studies. The risk of bias in the included papers was assessed using the prediction risk of bias assessment tool.

conclusionsAI techniques, including machine learning and deep learning models, have shown promise in improving diagnostic accuracy, predicting treatment outcomes, and identifying prognostic biomarkers. Despite these advancements, challenges persist, such as reliance on retrospective data, variability in imaging protocols, small sample sizes, and data preprocessing and model interpretability issues. These challenges limit the generalizability, clinical application, and integration of AI models.

Indexed as

artificial intelligencegastrointestinal cancermetastasissystematic review

Identifiers

PMID39941923
PMCPMC11817159

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.