Bridging the Precancerous Gap in Colorectal Cancer Through AI-Enhanced Liquid Biopsy, Endoscopy, and Digital Pathology

Colorectal cancer remains a leading cause of cancer-related mortality despite a prolonged and preventable precancerous phase. This review examines how artificial intelligence and emerging biomarkers may narrow the persistent gap between detecting established cancer and identifying advanced precursor lesions. Blood-based assays integrating cell-free DNA methylation, mutations, fragmentomic patterns, and other analytes achieve encouraging sensitivity for invasive colorectal cancer but remain substantially less sensitive for advanced adenomas and sessile serrated lesions, particularly in prospective average-risk populations. Machine-learning methods can integrate complementary molecular signals, although many models remain limited by case–control designs, spectrum bias, and inadequate external validation. Computer-aided detection increases adenoma detection and reduces miss rates, but benefits for advanced neoplasia, serrated lesions, colorectal cancer incidence, and mortality remain uncertain. AI-augmented digital pathology may improve polyp classification, dysplasia grading, invasive carcinoma recognition, and case prioritization after lesion removal. The greatest clinical value of these technologies is likely to arise from coordinated use within a multimodal, risk-adapted pathway that expands screening participation, prioritizes colonoscopy for individuals at greatest risk, and preserves high-quality colonoscopy and polypectomy as the central preventive intervention.