Advancements in artificial intelligence for colorectal cancer: A comprehensive overview of systematic reviews

Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Computational intelligence (CI) has emerged as a promising tool to improve diagnosis, staging, and treatment, but evidence remains scattered across the literature. This tertiary review aims to synthesize systematic reviews on CI applications in CRC care, highlighting algorithms, datasets, performance metrics, clinical scopes, and methodological gaps. A structured search in PubMed and EMBASE identified systematic reviews published between 2018 and 2023, following PRISMA guidelines. Twenty-two reviews were included. Extracted data covered CI techniques, evaluation methods, target outcomes, and dataset characteristics. Risk of bias was assessed using AMSTAR 2, and overlap of primary studies was analyzed through a correlation matrix. The reviews addressed four clinical scopes: macroscopic lesion classification (colonoscopy), histological analysis, disease staging, and survival or treatment prediction. Convolutional neural networks (CNNs) were the most commonly used models. While some applications showed high performance (AUC > 0.90), most reviews had low to moderate methodological quality. Key limitations included lack of external validation, dataset heterogeneity, and limited generalizability. Significant overlap was observed in studies focused on colonoscopy-based tasks. CI offers valuable contributions to CRC management, but broader clinical adoption is hindered by methodological inconsistencies and insufficient validation. This review provides a comprehensive synthesis to guide future research and promote the development of robust, explainable, and generalizable models for clinical use. • AI-driven advancements in colorectal cancer care: Enhancements in detection, diagnosis, prognosis, and treatment accuracy. • Early detection and personalized treatment: Promising applications in early polyp detection and personalized treatment response prediction. • Addressing CI challenges: Importance of model transparency, interpretability, and access to high-quality training data. • Recommendations for improvement: Enhancing CI applications through data augmentation and diverse dataset integration. • Future directions in CI for colorectal cancer: Need for interpretable models, standardized protocols, and improved cancer staging accuracy for better treatment decisions.