TY - JOUR
T1 - Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer
T2 - A Systematic Literature Review
AU - Naemi, Amin
AU - Tashk, Ashkan
AU - Azar, Amir Sorayaie
AU - Samimi, Tahereh
AU - Tavassoli, Ghanbar
AU - Mohasefi, Anita Bagherzadeh
AU - Khanshan, Elaheh Nasiri
AU - Najafabad, Mehrdad Heshmat
AU - Tarighi, Vafa
AU - Wiil, Uffe Kock
AU - Mohasefi, Jamshid Bagherzadeh
AU - Pirnejad, Habibollah
AU - Niazkhani, Zahra
PY - 2025/2/6
Y1 - 2025/2/6
N2 - 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.
AB - 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.
U2 - 10.3390/cancers17030558
DO - 10.3390/cancers17030558
M3 - Journal article
SN - 2072-6694
VL - 17
JO - Cancers
JF - Cancers
IS - 3
M1 - 558
ER -