Machine learning applications in colorectal cancer: from early detection to personalized treatment.

Colorectal cancer (CRC) is a significant health challenge in the world, with incidence being increasingly reported among the young population. Machine learning, therefore, is revolutionizing care in CRC, including providing advancements in early detection, staging, recurrence prediction, and individualized medicine. Techniques for analysis include support vector machines, random forests, and neural networks, which allow complex analyses of datasets, including genetic profiles and imaging data, with an improvement in diagnostic accuracy and treatment outcomes. Machine learning-driven personalized treatment strategies empower clinicians to tailor therapies to individual patients, optimizing efficacy while reducing side effects. However, integration of Machine learning (ML) in CRC management faces challenges like data quality, validation, and smooth adaptation into clinical workflow. Overcoming these barriers through multi-institutional collaboration and strong validation frameworks will be essential to unlock the full potential of ML. Advancement in research will enable the transformation of CRC care to provide more accurate diagnoses and targeted treatments, ultimately changing patient outcomes. Insight box This review examines the transformative impact of machine learning (ML) in colorectal cancer (CRC) research and care. By integrating multi-omics, radiomics, and clinical data, ML models outperform traditional diagnostic and prognostic methods, enabling precise risk prediction, personalized treatment, and early recurrence detection. The amalgamation of supervised learning, neural networks, and deep learning yields actionable insights that improve patient outcomes and address unmet needs in CRC management. The review also discusses solutions to challenges such as data standardization, ethics, and clinical workflow integration, offering a roadmap for real-world ML adoption. This work highlights the synergy between computational advances and oncology, providing a forward-thinking framework for CRC care.