Artificial Intelligence in Early Detection of Colorectal Cancer: Current Applications and Future Prospects

AbstractColorectal cancer (CRC) remains one of the most prevalent and deadly cancers worldwide, with early detection being critical for improving patient survival and reducing treatment costs. Traditional diagnostic methods, such as colonoscopy and histopathology, are effective but have limitations, including operator dependency, time consumption, and potential oversight of early-stage lesions. Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning, have opened new avenues for the early detection of CRC. AI-powered tools have demonstrated high accuracy in real-time polyp detection during colonoscopy, automated histological classification, and analysis of radiological and molecular data. These technologies promise not only enhanced diagnostic precision but also the potential for personalized screening strategies based on patient-specific risk profiles. Despite these advancements, challenges remain regarding data standardization, regulatory approval, clinical integration, and algorithm transparency. This review explores current applications of AI in CRC screening and outlines future prospects, emphasizing the transformative role of AI in revolutionizing cancer diagnostics. By overcoming existing barriers, AI can significantly contribute to reducing global CRC burden through earlier, more accurate, and more accessible detection methods.

Keywords:

Artificial intelligence , colorectal cancer , early detection , Machine Learning , colonoscopy