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

Overview

Journal Cancers (Basel)

Publisher MDPI

Specialty Oncology

Date 2025 Feb 13

PMID 39941923

Authors

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

Jamshid Bagherzadeh Mohasefi

Habibollah Pirnejad

Zahra Niazkhani

Affiliations

Soon will be listed here.

Abstract

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

Methods: The 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.

Results: forty-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.

Conclusions: AI 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.

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