AI for colon cancer: A focus on classification, detection, and predictive modeling

Abstract

PURPOSE: Artificial Intelligence (AI) is increasingly recognized for its potential in improving the detection, classification, prediction, and segmentation of colon cancer. Yet, the reliability of these applications depends on the quality and completeness of the underlying studies. This systematic review evaluates the current state of AI applications in colon cancer research, focusing on their impact on diagnostic accuracy, treatment planning, and patient outcomes. METHODS: A comprehensive search was conducted in PubMed, Scopus, and Web of Science for articles published between 2020 and 2024. The quality of the included studies was assessed using standardized criteria. A meta-analysis was performed where applicable, and a subgroup analysis was conducted based on the type of AI technology (e.g., deep learning, machine learning) and its application (detection, classification, etc.). Additionally, we recorded whether each study incorporated Explainable AI (XAI) techniques or Generative AI (e.g., GANs) as part of its methodology. RESULTS: In 80 articles, AI models showed significant improvements in diagnostic accuracy, particularly in polyp detection during colonoscopies and histopathological analysis. Deep learning approaches often outperformed traditional methods. However, clinical integration remains challenging due to data and validation gaps. CONCLUSION: AI holds great promise in colon cancer diagnosis and treatment. Future work should focus on integrating AI tools into clinical workflows through explainable models and standardized validation.