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. 2025 Jun 14;31(22):106500.
doi: 10.3748/wjg.v31.i22.106500.

Is the use of artificial intelligence the main stage for detecting polyps during colonoscopy?

Affiliations

Is the use of artificial intelligence the main stage for detecting polyps during colonoscopy?

Vladislav V Tsukanov et al. World J Gastroenterol. .

Abstract

Colorectal cancer (CRC) is the third most frequently diagnosed cancer and the second leading cause of cancer death worldwide. In this regard, CRC screening is one of the most important issues in modern preventive medicine. Colorectal polyps are potential predictors of CRC, and therefore represent one of the leading targets for screening colonoscopy. The difficulty of analyzing the information obtained during colonoscopy, including the size, location, shape, type of polyps, the need to standardize morphological data, determines that recently a number of works have promoted the opinion on the advisability of using various artificial intelligence (AI) methods to improve the effectiveness of endoscopic screening for CRC. At the same time, they point to a number of errors and methodological problems in the use of AI systems for the diagnosis of colorectal polyps. In this regard, the interpretation of the work of Shi et al, devoted to the use of a machine learning-based predictive model for monitoring the results of colorectal polypectomy, is undoubtedly interesting. In our opinion, the prospects for using AI to assess endoscopic screening for CRC look certainly positive, but the road to its widespread use will not be easy.

Keywords: Artificial intelligence; Colonoscopy; Colorectal cancer; Colorectal polyps; Diagnostics.

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Conflict of interest statement

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

References

    1. Shi YH, Liu JL, Cheng CC, Li WL, Sun H, Zhou XL, Wei H, Fei SJ. Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection. World J Gastroenterol. 2025;31:102387. - PMC - PubMed
    1. Tsukanov VV, Vasyutin AV, Tonkikh JL. Risk factors, prevention and screening of colorectal cancer: A rising problem. World J Gastroenterol. 2025;31:98629. - PMC - PubMed
    1. Dornblaser D, Young S, Shaukat A. Colon polyps: updates in classification and management. Curr Opin Gastroenterol. 2024;40:14–20. - PubMed
    1. Copland AP, Kahi CJ, Ko CW, Ginsberg GG. AGA Clinical Practice Update on Appropriate and Tailored Polypectomy: Expert Review. Clin Gastroenterol Hepatol. 2024;22:470–479.e5. - PubMed
    1. Knudsen MD, Wang K, Wang L, Polychronidis G, Berstad P, Hjartåker A, Fang Z, Ogino S, Chan AT, Song M. Colorectal Cancer Incidence and Mortality After Negative Colonoscopy Screening Results. JAMA Oncol. 2025;11:46–54. - PMC - PubMed