Authors : Mohamed Arsath Shamsudeen; Shifan Arif; Ayesha Zaffer Khanday; Syed Faazil Kazi; Arqam Mibsaam Ahmad; Faaiza Kazi

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/44yw8eak

Scribd : https://tinyurl.com/mry3tebt

DOI : https://doi.org/10.38124/ijisrt/25jul849

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Abstract : Colorectal cancer (CRC) recurrence after surgery is a major concern for patient prognosis and survival, making accurate and timely detection necessary. While imaging, biomarker analysis, and colonoscopies are important post-operative surveillance techniques, their sensitivity and specificity are often constrained. In recent years, artificial intelligence (AI) has emerged as a powerful tool for improving the identification and prognosis of colorectal cancer recurrence. Artificial intelligence (AI) algorithms, particularly ones built on machine learning (ML) and deep learning (DL), have shown great promise in the analysis of complicated medical data, including genetic profiles, histological slides, medical imaging, and patient clinical histories. By identifying subtle patterns that may be prone to be overlooked by clinicians, these systems have the potential to increase diagnostic accuracy and detect recurrences early. This study reviews recent developments, applications, and difficulties in the use of AI in the post-operative surveillance of colorectal cancer. It highlights AI-powered methods across genetics, pathology, and radiology, emphasising their potential incorporation into clinical practice for predictive and individualized recurrence monitoring. Additionally, the paper addresses the prospects of AI technology in the battle against colorectal cancer recurrence, as well as its ethical and regulatory considerations essential for their effective implementation into clinical practice.

Keywords : Artificial Intelligence, Machine Learning, Deep Learning, Colorectal Cancer, Recurrence, Detection

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