Artificial intelligence technologies in digestive system endoscopy: the state of the problem and prospects (literature review)

The review presents a comprehensive analysis of the current state and prospects for the use of artificial intelligence (AI) technologies in endoscopy of the digestive system. The research covers the main areas of AI implementation in endoscopic practice, including CADe and CADx computer vision systems, machine learning methods and deep learning algorithms. The paper examines the features of endoscopic procedures that affect the effectiveness of AI technologies: patient preparation, imaging quality depending on the skills of the endoscopist, and the multimodality of modern endoscopic methods. The results demonstrate the active development of AI technologies in endoscopy, especially in the field of detecting pathological changes in the gastrointestinal tract. Key applications of AI include cancer detection, diagnosis of Helicobacter pylori, assessment of inflammatory diseases, and quality control of research. The analysis shows that despite significant advances in the development of AI systems for endoscopy, their implementation is limited by a number of factors, including dependence on the operator and the complexity of standardization. In the near future, new approaches will be introduced to train AI models, including recurrent neural networks and multimodal AI systems that combine visual data with other patient information.

1. Qin X., Ran T., Chen Y., et al. Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges. Diagnostics (Basel). 2023;13(19):30–54. DOI: 10.3390/diagnostics13193054.

2. Abonamah A.A., Tariq M.U., Shilbayeh S. On the Commoditization of Artificial Intelligence. Front. Psychol. 2021;12:696346.

3. Schöler J., Alavanja M., de Lange T., et al. Impact of AI-aided colonoscopy in clinical practice: a prospective randomised controlled trial. BMJ Open Gastroenterol. 2024;11(1):e001247. DOI: 10.1136/bmjgast-2023-001247.

4. Okagawa Y., Abe S., Yamada M., et a. Artificial Intelligence in Endoscopy. Digestive Diseases and Sciences. 2021. DOI: 10.1007/s10620-021-07086-z.

5. Cai S.L., Li B., Tan W.M., et al. Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). Gastrointest Endosc. 2019;90:745–753.e2. DOI: 10.1016/j.gie.2019.06.044.

6. Guo L., Xiao X., Wu C. et al. Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos). Gastrointest Endosc. 2020;91:41–51.

7. Piazuelo M.B., Bravo L.E., Mera R.M., et al. The Colombian chemoprevention trial: 20-year follow-up of a cohort of patients with gastric precancerous lesions. Gastroenterology 2021;160:1106–1117.e3. DOI: 10.1053/j.gastro.2020.11.017.

8. de Groof A.J., Struyvenberg M.R., van der Putten J., et al. Deep-learning system detects neoplasia in patients with Barrett’s esophagus with higher accuracy than endoscopists in a multi-step training and validation study with benchmarking. Gastroenterology. 2020;158:915–929. DOI: 10.1053/j.gastro.2019.11.030.

9. Trindade A.J., McKinley M.J., Fan C., et al. Endoscopic Surveillance of Barrett's Esophagus Using Volumetric Laser Endomicroscopy with Artificial Intelligence Image Enhancement. Gastroenterology. 2019;157:303–305. DOI: 10.1053/j.gastro.2019.04.048.

10. Hu Chen, Shi-Yu Liu, Si-Hui Huang, et al. Applications of artificial intelligence in gastroscopy: a narrative review. J Int Med Res. 2024;52(1):3000605231223454. DOI: 10.1177/03000605231223454.

11. Hosokawa O., Hattori M., Douden K., et al. Difference in accuracy between gastroscopy and colonoscopy for detection of cancer. Hepatogastroenterology. 2007;54:442–444.

12. Du H., Dong Z., Wu L., et al. A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video) Gastric Cancer. 2023;26:275–285. DOI: 10.1007/s10120-022-01358-x.

13. Wu L., Zhou W., Wan X., et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy. 2019;51:522–531. DOI: 10.1055/a-0855-3532.

14. Lee J.H., Kim Y.J., Kim Y.W., et al. Spotting malignancies from gastric endoscopic images using deep learning. Surg Endosc. 2019;33:3790–3797. DOI: 10.1007/s00464-019-06677-2.

15. Nagao S., Tsuji Y., Sakaguchi Y., et al. Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigocarmine dye contrast imaging. Gastrointest Endosc. 2020;92:866–873.e1. DOI: 10.1016/j.gie.2020.06.047.

16. Ito N., Kawahira H., Nakashima H., et al. Endoscopic Diagnostic Support System for cT1b Colorectal Cancer Using Deep Learning. Oncology. 2019;96(1):44–50. DOI: 10.1159/000491636.

17. Kominami Y., Yoshida S., Tanaka S., et al. Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest Endosc. 2016;83(3):643–9. DOI: 10.1016/j.gie.2015.08.004.

18. Fernández-Esparrach G., Bernal J., López-Cerón M., et al. Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. Endoscopy. 2016;48(9):837–42. DOI: 10.1055/s-0042-108434.

19. Misawa M., Kudo S.E., Mori Y., et al. Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video). Gastrointest Endosc. 2021;93(4):960–967.e3. DOI: 10.1016/j.gie.2020.07.060.

20. Sánchez-Montes C., Sánchez F.J., Bernal J., et al. Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis. Endoscopy. 2019;51(3):261–265. DOI: 10.1055/a-0732-5250.

21. Kulaev K., Vazhenin A., Rostovtsev D., et al. Artificial intelligence in the diagnosis of neoplasms of the large intestine-development, implementation of technology and first results. Issues of Oncology. 2023;69(2):292–299. DOI: 10.37469/0507-3758-2023-69-2-292-299 (In Russ.).

22. Xu H., Tang R.S.Y., Lam T.Y.T., et al. Artificial Intelligence-Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial. Clin Gastroenterol Hepatol. 2023;21(2):337–346.e3. DOI: 10.1016/j.cgh.2022.07.006.

23. Lin C.H., Hsu P.I., Tseng C.D., et al. Application of artificial intelligence in endoscopic image analysis for the diagnosis of a gastric cancer pathogen-Helicobacter pylori infection. Sci Rep. 2023;13(1):13380. DOI: 10.1038/s41598-023-40179-5.

24. Turtoi D.C., Brata V.D., Incze V., et al. Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review. J Clin Med. 2024;13(16):4818. DOI: 10.3390/jcm13164818.

25. Shi Y., Wei N., Wang K., et al. Diagnostic value of artificial intelligence-assisted endoscopy for chronic atrophic gastritis: a systematic review and meta-analysis. Front Med (Lausanne). 2023;10:1134980. DOI: 10.3389/fmed.2023.1134980.

26. Tenório J. M., Hummel A. D., Cohrs F. M., et al. Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease. Int J Med Inform. 2011;80(11):793–802. DOI: 10.1016/j.ijmedinf.2011.08.001.

27. Wang X., Qian H., Ciaccio E.J., et al. Celiac disease diagnosis from videocapsule endoscopy images with residual learning and deep feature extraction. Comput Methods Programs Biomed. 2020;187:105236. DOI: 10.1016/j.cmpb.2019.105236.

28. Khorasani H.M., Usefi H., Peña-Castillo L. Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning. Sci Rep. 2020;10(1):13744. DOI: 10.1038/s41598-020-70583-0.

29. Maeda Y., Kudo S.E., Mori Y., et al. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video). Gastrointest Endosc. 2019;89(2):408–415. DOI: 10.1016/j.gie.2018.09.024.

30. Cui H., Zhao Y., Xiong S., et al. Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence: A Randomized Crossover Trial. JAMA Netw Open. 2024;7(7):e2422454. DOI: 10.1001/jamanetworkopen.2024.22454.

31. Tonozuka R., Itoi T., Nagata N., et al. Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study. J Hepatobiliary Pancreat Sci. 2021;28(1):95–104. DOI: 10.1002/jhbp.825.

32. Kuwahara T., Hara K., Mizuno N., et al. Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses. Endoscopy. 2023;55(2):140–149. DOI: 10.1055/a-1873-7920.

33. Marya N.B., Powers P.D., Chari S.T., et al. Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis. Gut. 2021;70(7):1335–1344. DOI: 10.1136/gutjnl-2020-322821.

34. Kuwahara T., Hara K., Mizuno N., et al. Usefulness of Deep Learning Analysis for the Diagnosis of Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas. Clin Transl Gastroenterol. 2019;10(5):1–8. DOI: 10.14309/ctg.0000000000000045.

35. Wu H.L., Yao L.W., Shi H.Y., et al. Validation of a real-time biliopancreatic endoscopic ultrasonography analytical device in China: a prospective, single-centre, randomised, controlled trial. Lancet Digit Health. 2023;5(11):e812–e820. DOI: 10.1016/S2589-7500(23)00160-7.

36. Zhang J., Zhu L., Yao L., et al. Deep learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video). Gastrointest Endosc. 2020;92(4):874–885.e3. DOI: 10.1016/j.gie.2020.04.071.

37. Zhang B., Zhu F., Li P., Zhu J. Artificial intelligence-assisted endoscopic ultrasound in the diagnosis of gastrointestinal stromal tumors: a meta-analysis. Surg Endosc. 2023;37(3):1649–1657. DOI: 10.1007/s00464-022-09597-w.

38. Tanaka H., Kamata K., Ishihara R., et al. Value of artificial intelligence with novel tumor tracking technology in the diagnosis of gastric submucosal tumors by contrast-enhanced harmonic endoscopic ultrasonography. J Gastroenterol Hepatol. 2022;37(5):841–846. DOI: 10.1111/jgh.15780.

39. Zhang X., Tang D., Zhou J.D., et al. A real-time interpretable artificial intelligence model for the cholangioscopic diagnosis of malignant biliary stricture (with videos). Gastrointest Endosc. 2023;98(2):199–210.e10. DOI: 10.1016/j.gie.2023.02.026.

40. Saraiva M.M., Ribeiro T., Ferreira J.P.S., et al. Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: A pilot study. Gastrointest. Endosc. 2022;95:339–348.

41. Agudo Castillo B., Mascarenhas M., Martins M., et al. Advancements in biliopancreatic endoscopy - A comprehensive review of artificial intelligence in EUS and ERCP. Rev Esp Enferm Dig. 2024;116(11):613–622. DOI: 10.17235/reed.2024.10456/2024.

42. Aoki T., Yamada A., Kato Y., et al. Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network. J Gastroenterol Hepatol. 2020;35(7):1196–1200. DOI: 10.1111/jgh.14941.

43. Ding Z., Shi H., Zhang H., et al. Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model. Gastroenterology. 2019;157(4):1044–1054.e5. DOI: 10.1053/j.gastro.2019.06.025.

44. Fu Y., Zhang W., Mandal M., Meng M.Q. Computer-aided bleeding detection in WCE video. IEEE J Biomed Health Inform. 2014;18(2):636–42. DOI: 10.1109/JBHI.2013.2257819.

45. Tsuboi A., Oka S., Aoyama K., et al. Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images. Dig Endosc. 2020;32(3):382–390. DOI: 10.1111/den.13507.

46. Otani K., Nakada A., Kurose Y., et al. Automatic detection of different types of small-bowel lesions on capsule endoscopy images using a newly developed deep convolutional neural network. Endoscopy. 2020;52(9):786–791. DOI: 10.1055/a-1167-8157.

47. Rey J.F. Magnetically guided gastric capsule endoscopy: a review and new developments. Clin Endosc. 2025. DOI: 10.5946/ce.2025.062.

48. An P., Yang D., Wang J., et al. A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy. Gastric Cancer. 2020;23:884–892. DOI: 10.1007/s10120-020-01071-7.

49. Thakkar S., Carleton N. M., Rao B., Syed A. Use of Artificial Intelligence-Based Analytics from Live Colonoscopies to Optimize the Quality of the Colonoscopy Examination in Real Time: Proof of Concept. Gastroenterology. 2020;158(5):1219–1221.e2. DOI: 10.1053/j.gastro.2019.12.035.

50. Leenhardt R., Souchaud M., Houist G., et al. A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy. Endoscopy. 2021;53(9):932–936. DOI: 10.1055/a-1301-3841.

51. Wu L., Zhang J., Zhou W., et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut. 2019;68(12):2161–2169. DOI: 10.1136/gutjnl-2018-317366.

52. Yao L., Zhang L., Liu J., et al. Effect of an artificial intelligence-based quality improvement system on efficacy of a computer-aided detection system in colonoscopy: a fourgroup parallel study. Endoscopy. 2022;54(8):757–768. DOI: 10.1055/a-1706-6174.

53. Parasa S., Wallace M., Bagci U., et al. Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit. Gastrointest Endosc 2020;92:938–945.e1. DOI: 10.1016/j.gie.2020.04.044.

54. Messmann H., Bisschops R., Antonelli G., et al. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy. 2022;54(12):1211–1231. DOI: 10.1055/a-1950-5694.

55. Kröner P. T., Engels M. M., Glicksberg B. S., et al. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol. 2021;27(40):6794–6824. DOI: 10.3748/wjg.v27.i40.6794.

56. Zhang J., Liu L., Xiang P., et al. AI co-pilot bronchoscope robot. Nat Commun. 2024;15(1):241. DOI: 10.1038/s41467-023-44385-7.

57. Takamatsu T., Endo Y., Fukushima R., et al. Robotic endoscope with double-balloon and double-bend tube for colonoscopy. Sci Rep. 2023;13(1):10494. DOI: 10.1038/s41598-023-37566-3.

58. Ahmed J. F., Coda S., Premchand P., et al. A UK single-center pilot experience using a novel robotic inchworm colonoscopy system. DEN Open. 2025;6(1):e70123. DOI: 10.1002/deo2.70123.

59. Lebedev A. A., Khryashchev V. V., Kashin S. V., et al. Application of deep learning methods to support medical decision-making during endoscopic examination of the stomach. Models, Systems, Networks in Economics, Engineering, Nature and Society. 2021;2:95–106. DOI: 10.21685/2227-8486-2021-2-6. (In Russ.).

60. Khryashchev V. V. Decision support system for medical decision-making using an artificial intelligence module for endoscopic examinations of the stomach. Medical Equipment (Meditsinskaya tekhnika). 2023;6:44–47 (In Russ.).