Artificial intelligence for early detection and diagnosis of colorectal cancer: Current evidence and future directions

Abstract

Colorectal cancer (CRC) is one of the most prevalent and deadly malignancies globally. Early detection and accurate diagnosis are crucial for improving survival rates and treatment outcomes. Research in oncology and medical informatics has made significant progress. However, conventional screening approaches, such as colonoscopies, fecal occult blood tests, and imaging modalities, have limitations in terms of sensitivity, specificity, and patient adherence. Thus, a literature review was conducted to identify contemporary evidence relevant to the study objectives. Searches were conducted across major scientific databases, including PubMed, EBSCO, and Scopus. The search strategy targeted peer-reviewed publications encompassing original research articles, systematic reviews, and expert commentaries published within the past 5 years. High-impact journals, such as The New England Journal of Medicine and Nature were manually screened to ensure the inclusion of seminal and authoritative works. Notably, advances in artificial intelligence (AI), software detection tools, models, and algorithms have enabled improved screening, rapid detection, risk prediction, and more accurate and consistent diagnosis. Real-time AI-assisted endoscopy has improved detection rates for colorectal neoplasia. The review indicated that the adenoma detection rate during colonoscopy was 54.8% in the computer-aided polyp detection group compared to 40.4% in the control group. The incorporation of AI in the early detection and diagnosis of CRC presents considerable potential. While the use of these tools has potential benefits, challenges remain. Ongoing efforts are focused on overcoming barriers to clinical integration and improving clinical outcomes, ensuring that AI technologies are safe, effective, and accessible.

Keywords

Colorectal cancer / Artificial intelligence / Precision oncology / Cancer screening / Therapy / Computer-aided diagnosis

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