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image of The Impact of Artificial Intelligence Techniques and Machine Learning on Colorectal Cancer Management

Abstract

Bowel cancer, known as colorectal cancer (CRC), is among the most common types of newly diagnosed cancers and a leading cause of cancer-related deaths. Despite advances in medical technology and screening programs, gaps in the detection of colorectal cancer patients persist, leading to delayed diagnoses and poorer outcomes. Therefore, new approaches using artificial intelligence-based analysis with gene panels and traditional risk factors for risk prediction and identification of cases at high risk are urgently warranted. Artificial Intelligence (AI) has emerged as a promising tool to enhance early detection and screening efficacy. Moreover, early detection is crucial for successful treatment and improved survival rates. However, conventional screening methods, such as colonoscopy and fecal occult blood tests (FOBT), have their limitations, including cost, invasiveness, and patient compliance. As a result, many individuals go undiagnosed until the disease has progressed to an advanced stage. In aggregate, the integration of AI in CRC detection holds great promise for bridging the existing gaps and improving patient outcomes. As technology continues to evolve, AI algorithms will become even more sophisticated, accurate, and scalable. Collaboration between clinicians, researchers, and AI developers is essential to harness the full potential of AI for earlier detection and better management of CRC, ultimately saving lives and reducing the global burden of disease.

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2025-09-15
2025-12-17

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/content/journals/cpd/10.2174/0113816128377312250827213457
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The Impact of Artificial Intelligence Techniques and Machine Learning on Colorectal Cancer Management
Bentham Science Publishers ; https://doi.org/10.2174/0113816128377312250827213457
/content/journals/cpd/10.2174/0113816128377312250827213457
/content/journals/cpd/10.2174/0113816128377312250827213457
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  • Article Type:     Review Article
Keywords: colorectal cancer ; Artificial intelligence ; diagnosis ; neural networks ; colonoscopy ; machine learning

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