Can Artificial Intelligence Revolutionize Colorectal Cancer Screening and Diagnosis?

Background: Colorectal cancer (CRC) remains a major contributor to cancer-related mortality globally, with a concerning increase in early-onset cases. Although colonoscopy is the established gold standard for CRC screening, its effectiveness is constrained by operator variability, inconsistent bowel preparation, and disparities in access. Artificial intelligence (AI) has emerged as a promising adjunct across the CRC care continuum, offering potential enhancements in screening, diagnosis, and risk stratification. This review aims to examine the current applications of AI in CRC screening and diagnosis, with particular emphasis on AI-assisted endoscopy, non-invasive screening modalities, and digital pathology. Key implementation challenges are also discussed. Methods: This narrative review synthesizes evidence from randomized controlled trials, prospective cohort studies, meta-analyses, and emerging translational research. It evaluates AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems in colonoscopy, machine learning–driven risk prediction models and biomarker integration for non-invasive screening, and deep learning applications in whole-slide histopathology for CRC. Results and Discussion: AI-assisted colonoscopy has demonstrated consistent improvements in adenoma detection rates, particularly through enhanced identification of diminutive lesions. Several multicenter trials have also reported increased detection of advanced adenomas. CADx systems employing enhanced imaging modalities have achieved optical diagnostic performance comparable to expert endoscopists and may facilitate cost-saving strategies such as “resect-and-discard” or “diagnose-and-leave.” Beyond the endoscopy suite, AI and machine learning algorithms can integrate multimodal data, including demographic, dietary, biomarker, and circulating cell-free DNA (cfDNA) profiles, to identify individuals at elevated risk and strengthen non-invasive screening approaches. In pathology, AI-powered systems have shown promise in reducing interobserver variability, detecting subtle morphologic and molecular features (e.g., microsatellite instability), and informing treatment planning. Despite these advances, translation into routine clinical practice remains limited by several factors: heterogeneity in training datasets, potential algorithmic bias, insufficient real-world validation, substantial infrastructure requirements, and the need for interpretable outputs that clinicians can trust. Addressing these barriers will be essential to ensure safe, and effective integration of AI into CRC care. Conclusion: Artificial intelligence holds substantial promise for enhancing the accuracy, scalability, and personalization of CRC screening and diagnosis. Realizing this potential will require rigorous multicenter prospective validation, standardization of datasets and reporting frameworks, and the development of clinical workflows that preserve provider judgment and promote equitable access to care.