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Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review
Amin Naemi1, Ashkan Tashk2, Amir Sorayaie Azar3,4
1Nordcee, Department of Biology, University of Southern Denmark, 5230 Odense, Denmark.
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.
Insights
Artificial Intelligence (AI) shows promise in diagnosing and treating metastatic gastrointestinal cancers. However, challenges like data issues and model interpretability hinder clinical use.
Area of Science:
- Oncology
- Medical Informatics
- Artificial Intelligence
Background:
- Systematic review on Artificial Intelligence (AI) applications in metastatic gastrointestinal (GI) cancers.
- Focus on AI's role in diagnosis, treatment, and follow-up stages.
Purpose of the Study:
- To systematically review the current applications of AI in managing metastatic GI cancers.
- To identify the potential and limitations of AI models in this field.
Main Methods:
- Comprehensive search of PubMed, Scopus, Embase, and Google Scholar databases.
- Inclusion of English-language articles published between January 2010 and January 2022.
- Systematic appraisal and risk of bias assessment of 46 selected studies.
Main Results:
- AI models, including machine learning and deep learning, demonstrate potential in enhancing diagnostic accuracy for metastatic GI cancers.
- AI shows promise in predicting treatment outcomes and identifying prognostic biomarkers.
- Forty-six studies were critically appraised, assessing the risk of bias.
Conclusions:
- AI techniques offer significant potential for improving the management of metastatic GI cancers.
- Challenges such as reliance on retrospective data, small sample sizes, and model interpretability impede clinical integration.
- Further research is needed to overcome these limitations for broader AI application in oncology.
