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<p>Artificial Intelligence (AI) is revolutionizing colonoscopy and promises to improve the quality and reliability of colorectal cancer screening. Today, there are three main areas of AI application in colonoscopy: 1.&#xa0;Computer-aided detection (CADe): these systems assist in real-time lesion detection and have significantly increased the adenoma detection rate (ADR) in studies. 2.&#xa0;Computer-aided diagnosis (CADx): this technology helps characterize and classify lesions, improves the accuracy of optical diagnosis and can reduce unnecessary polypectomy. 3.&#xa0;Computer-aided quality improvement (CAQ): these systems monitor and improve procedural aspects such as withdrawal time, bowel preparation and completeness of examination. Despite promising results challenges remain, including the need for larger validation studies, the risk of overdiagnosis and technical requirements. Future developments could include integrated systems, personalized risk predictions and automated reporting. The integration of AI into colonoscopy has the potential to improve colorectal cancer screening, increase ADR and standardize the quality of examinations. Further research and careful implementation are necessary to successfully integrate this technology into clinical practice.</p>

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Künstliche Intelligenz in der Koloskopie

  • 期刊论文

  • 发表日期：

    2025年7月24日

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  • Alexander Ziachehabi

摘要

Artificial Intelligence (AI) is revolutionizing colonoscopy and promises to improve the quality and reliability of colorectal cancer screening. Today, there are three main areas of AI application in colonoscopy: 1. Computer-aided detection (CADe): these systems assist in real-time lesion detection and have significantly increased the adenoma detection rate (ADR) in studies. 2. Computer-aided diagnosis (CADx): this technology helps characterize and classify lesions, improves the accuracy of optical diagnosis and can reduce unnecessary polypectomy. 3. Computer-aided quality improvement (CAQ): these systems monitor and improve procedural aspects such as withdrawal time, bowel preparation and completeness of examination. Despite promising results challenges remain, including the need for larger validation studies, the risk of overdiagnosis and technical requirements. Future developments could include integrated systems, personalized risk predictions and automated reporting. The integration of AI into colonoscopy has the potential to improve colorectal cancer screening, increase ADR and standardize the quality of examinations. Further research and careful implementation are necessary to successfully integrate this technology into clinical practice.