Explainable Machine Learning for Early Colorectal Cancer Detection: A Review of Recent Advances and Open Research Directions

Colorectal cancer continues to be the leading causes of death globally. The techniques of screening pathways and invasive procedures are limited early-stage sensitivity. This review explores the recent advances in explainable artificial intelligence (XAI) and machine learning (ML) techniques applied to colorectal cancer (CRC) screening, with an emphasis on multimodal fusion frameworks, and federated learning. We analyze typical clinical trials that highlight the advantages and disadvantages of the main methods (XGBoost, LightGBM, SHAP, LIME, Grad-CAM, and ResNet-style deep models) and show their mathematical underpinnings. Comparative analysis across technique families reveals limitations, such as restricted cross-site validation, inconsistent treatment of class imbalance, and limited evaluation of computational sustainability. In order to facilitate early detection, we advocate for a sustainable, comprehensible, multimodal architecture that combines structured patient metadata with blood-based biomarker signals, such as serum Raman spectroscopy.