(untitled) (Journal of University of Babylon for Engineering Sciences, 2024)
The increasing worldwide prevalence of colorectal cancer (CRC) emphasizes how urgently precancerous polyps must be found by regular colonoscopy. But for reasons including size, form, and visibility restrictions, even experienced endoscopists sometimes miss polyps. Using deep learning and convolutional neural networks (CNNs), artificial intelligence (AI) has become a transforming solution helping in real-time polyp detection with amazing sensitivity and accuracy. Focusing on the ability of AI-based systems to improve Adenoma identification Rates (ADR) and lower polyp miss rates, especially for difficult small or flat polyps, this article offers a thorough examination of recent AI breakthroughs in polyp identification. Important research show that by raising diagnosis accuracy and lowering endoscopist fatigue, artificial intelligence could enhance clinical practice. We also look at the pragmatic issues linked to introducing artificial intelligence systems into colonoscopy procedures including ethical, legal, and data-related ones. Looking ahead, developments in tailored detection technologies and ongoing improvement of AI algorithms present exciting paths to enhance outcomes in CRC prevention. This paper emphasizes how important artificial intelligence is in raising polyp detection standards and determining the direction of endoscopic medicine.