Colorectal cancer worldwide: epidemiological trends, economic burden, and the promise of AI-driven solutions

Abstract

Colorectal cancer (CRC) remains a significant global health challenge due to its high incidence and mortality, underscoring the need for early detection and precise diagnosis to improve survival outcomes. Recent advances in artificial intelligence (AI), particularly deep learning and machine learning (ML), have revolutionized medical imaging and reshaped CRC screening, diagnosis, and prognosis. AI algorithms demonstrate strong performance in analyzing computed tomography, magnetic resonance imaging, and endoscopic images, achieving superior sensitivity, specificity, and efficiency in detecting and characterizing colorectal lesions. These developments enhance lesion identification, risk stratification, and treatment planning, advancing the broader goal of precision medicine. Importantly, AI has the potential to reduce health disparities by extending access to high-quality diagnostic capabilities in low-resource regions where shortages of expert radiologists delay detection. Despite these advantages, implementation in clinical practice remains limited by several challenges, including data bias, lack of population diversity in training datasets, limited generalizability, operator dependency, and integration difficulties within existing workflows. Moreover, ethical and economic considerations—such as algorithm transparency, data privacy, and cost-effectiveness—continue to shape adoption. This review synthesizes current evidence on AI applications in CRC imaging, emphasizing methodological progress, clinical performance, and translational challenges. It also evaluates the readiness of AI systems for real-world use, highlighting ongoing needs for validation, regulatory oversight, and interdisciplinary collaboration. Ultimately, AI holds transformative potential to enhance CRC detection and management, improve diagnostic accuracy, and promote equitable access to advanced screening worldwide, provided that technological, ethical, and implementation barriers are effectively addressed.

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