Toward Precision Colorectal Cancer Screening: Integrating Clinical Risk Stratification, Molecular Biomarkers, and Artificial Intelligence

Colorectal cancer (CRC) screening reduces disease incidence and mortality through the detection and removal of precursor lesions and the earlier diagnosis of invasive cancer. However, most current programs remain predominantly age-based, applying broadly uniform tests and intervals to individuals whose underlying risk, lesion biology, screening adherence, and capacity to benefit may differ substantially. Advances in clinical prediction, molecular diagnostics, and artificial intelligence (AI) create opportunities to refine screening according to individual risk and clinical context. This narrative review examines the evolution of CRC screening from conventional population-based strategies toward a precision-screening framework comprising three complementary functions. Clinical risk stratification estimates background susceptibility using demographic, familial, behavioral, metabolic, medical, and previous-screening information. Molecular biomarkers provide biologically distinct information, ranging from inherited susceptibility to signals associated with prevalent colorectal neoplasia. AI may support multimodal risk integration, procedural lesion detection and characterization, and longitudinal reassessment as new clinical and screening information accumulates. These components should not be regarded as a mandatory sequential pathway or as interchangeable sources of evidence; rather, their value depends on the specific clinical question, predicted outcome, time horizon, and decision being supported. Current evidence shows that risk-adapted and multimodal approaches are feasible, but improved prediction or diagnostic performance does not necessarily translate into superior population outcomes. Most precision-screening strategies still lack prospective evidence demonstrating improvements in participation, diagnostic completion, advanced colorectal neoplasia yield, interval CRC, harms, resource use, equity, or cost-effectiveness compared with optimized established screening. The priority for translation is therefore not maximal technological integration, but selective use of additional information when it provides actionable incremental benefit while preserving the accessibility, effectiveness, and public-health strengths of organized screening.