Methods: Due to this demand, an enormous amount of research has been conducted. Therefore, in this study, we utilized a Systematic Literature Review (SLR) to encompass all facets of findings from relevant articles. Inspection of genetic factors inspection, CRC early detection, late-stage CRC rapid prediction, treatment plan selection, metastatic biomarkers discovery, CRC types classification, CRC risk prediction, and CRC survival rate prediction are the critical uses of applications employed in the CRC field.
Results: Random Forest (RF), Support Vector Machine (SVM), Conventional Neural Network (CNN), and other AI models are frequently used in such scenarios. A comprehensive assessment was conducted on the diverse issues and obstacles associated with implementing AI applications for this particular disease. According to the data, most papers are evaluated mainly on accuracy, delay time, data privacy, robustness, and dataset availability. ML models are employed in 50 % of the papers, while DL models account for 23.3 %. Furthermore, XAI is utilized in 10 % of cases, and hybrid models are implemented in 16.7 % of papers.
Conclusions: Inspection of genetic factors, early CRC detection, late-stage CRC rapid prediction, treatment plan selection, metastatic biomarkers discovery, CRC types classification, CRC risk prediction, and CRC survival rate prediction highlight the critical contributions of AI in the CRC field.