The site of the Media Sphera Publishers contains materials intended solely for healthcare professionals.
By closing this message, you confirm that you are a certified medical professional or a student of a medical educational institution.

Login
EN
+7 (495) 482-4118
+7 (495) 482-4329
+8 800 250-18-12
  • (toll-free number for subscriptions)
    Mon-Fri from 10 a.m. to 6 p.m.

  • © 1998-2026
+7 (495) 482-43-29
EN
All journals
Journal
Year
Year
Search result: 0

Samsonyan E.Kh.

Russian University of Medicine, Moscow, Russia;
Centrosoyuz Hospital of the Russian Federation, Moscow, Russia

Parkhomenko K.A.

Russian University of Medicine, Moscow, Russia

Emelyanov S.I.

Russian University of Medicine, Moscow, Russia

The evolution of artificial intelligence technologies in endoscopy and an analysis of its potential

Authors:

Samsonyan E.Kh., Parkhomenko K.A., Emelyanov S.I.

More about the authors

Journal: Endoscopic Surgery. 2026;32(2): 63‑72

DOI: 10.17116/endoskop20263202163

Read: 235 times

Contact the author
Table of contents

THE EVOLUTION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN ENDOSCOPY AND AN ANALYSIS OF ITS POTENTIAL
THE EVOLUTION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN ENDOSCOPY AND AN ANALYSIS OF ITS POTENTIAL

To cite this article:

Samsonyan EKh, Parkhomenko KA, Emelyanov SI. The evolution of artificial intelligence technologies in endoscopy and an analysis of its potential. Endoscopic Surgery. 2026;32(2):63‑72. (In Russ.)
https://doi.org/10.17116/endoskop20263202163

Подписка 2026 Эндоскопическая хирургия (Endoscopic Surgery)
МСФ на ЯМ
Использование инфографики авторами научных работ
Recommended articles:
Prognostic model and calculator for asse­ssing the risk of prolonged pleural effu­sion after lobe­ctomy. Piro­gov Russian Journal of Surgery. 2025;(6):26-34
Arti­ficial inte­lligence capa­bilities in multiple scle­rosis. S.S. Korsakov Journal of Neurology and Psychiatry. 2025;(5):14-21
Complications in diagnostic and therapeutic endo­scopic procedures on the lower gastrointestinal tract. Collegial discussion of info­rmed consent by the Asso­ciation of Quality Care Expe­rts. Russian Journal of Evidence-Based Gastroenterology. 2025;(2):94-105
Arti­ficial inte­lligence in surgery. Redu­cing the risks rela­ted to poly­pharmacy in perioperative period. Piro­gov Russian Journal of Surgery. 2025;(7):59-66
Arti­ficial inte­lligence in comprehensive reha­bilitation of people with disa­bilities. (Literature review). Problems of Balneology, Physiotherapy and Exercise Therapy. 2025;(3):54-61
The Webiomed.DHRA deci­sion-making support system for medi­cal examinations in the Russian Fede­ration: a cost-effectiveness analysis. Medi­cal Technologies. Asse­ssment and Choice. 2025;(3):61-73
Arti­ficial inte­lligence in reproductive medi­cine and surgery. Russian Journal of Human Reproduction. 2025;(4):10-25
Arti­ficial inte­lligence capa­bilities in choosing surgical treatment for tetralogy of Fallot. Russian Journal of Cardiology and Cardiovascular Surgery. 2025;(5):560-565
Problems and solu­tions in applying machine learning algo­rithms for data analysis in cardiology. Russian Journal of Preventive Medi­cine. 2025;(10):16-22
Socio-demographic portrait of a patient with endo­metriosis using arti­ficial inte­lligence (original mate­rial). Russian Journal of Human Reproduction. 2025;(5):84-88
Abstract:

In recent years, artificial intelligence (AI) has been increasingly integrated into endoscopic practice and is considered a priority area for the development of visual diagnostics. This analysis highlights developments aimed at automated detection and characterization of gastrointestinal tumors, assessment of invasion depth, monitoring the completeness and quality of examinations, standardization of photo documentation, risk stratification, and training support for novice endoscopists. The objective of this review is to summarize the current state of AI technologies in endoscopy, assess their clinical significance, and identify key limitations that hinder their widespread use in clinical practice. The data presented demonstrate that modern algorithms can improve diagnostic efficiency and reduce inter-specialist variability. The use of AI helps mitigate the influence of human factors and experience on the quality of visual examinations and can also serve as a tool for developing practical experience in novice endoscopists. This demonstrates the high potential of AI not only as a diagnostic tool but also as a means of process optimization and training support. However, there remains a need for further multicenter studies, the development of more comprehensive and integrated solutions, and the assessment of the long-term impact of modern algorithms on clinical practice.

Keywords:

artificial intelligence
colonoscopy
esophagogastroduodenoscopy
automated detection
examination quality
endoscopic image analysis
gastrointestinal tumors

Authors:

Samsonyan E.Kh.

Russian University of Medicine, Moscow, Russia;
Centrosoyuz Hospital of the Russian Federation, Moscow, Russia

Parkhomenko K.A.

Russian University of Medicine, Moscow, Russia

Emelyanov S.I.

Russian University of Medicine, Moscow, Russia

Received:

16.12.2025

Accepted:

25.12.2025

Close metadata

References:

  1. Karkanis SA, Iakovidis DK, Maroulis DE, Karras DA, Tzivras M. Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans Inf Technol Biomed. 2003;7(3):141-152.  https://doi.org/10.1109/titb.2003.813794
  2. Arif AA, Jiang SX, Byrne MF. Artificial intelligence in endoscopy: Overview, applications, and future directions. Saudi J Gastroenterol. 2023;29(5):269-277.  https://doi.org/10.4103/sjg.sjg_286_23
  3. Ishtiaq R, Zulfiqar L, Gangwani MK, Aziz M. Adenoma detection rate vs. adenoma per colonoscopy as quality indicators for colon cancer screening. Transl Gastroenterol Hepatol. 2023;8:24.  https://doi.org/10.21037/tgh-22-92
  4. Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, Liu P, Li L, Song Y, Zhang D, Li Y, Xu G, Tu M, Liu X. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019;68(10):1813-1819. https://doi.org/10.1136/gutjnl-2018-317500
  5. Gong D, Wu L, Zhang J, Mu G, Shen L, Liu J, Wang Z, Zhou W, An P, Huang X, Jiang X, Li Y, Wan X, Hu S, Chen Y, Hu X, Xu Y, Zhu X, Li S, Yao L, He X, Chen D, Huang L, Wei X, Wang X, Yu H. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol. 2020;5(4):352-361.  https://doi.org/10.1016/S2468-1253(19)30413-3
  6. Karsenti D, Tharsis G, Perrot B, Cattan P, Percie du Sert A, Venezia F, Zrihen E, Gillet A, Lab JP, Tordjman G, Cavicchi M. Effect of real-time computer-aided detection of colorectal adenoma in routine colonoscopy (COLO-GENIUS): a single-centre randomised controlled trial. Lancet Gastroenterol Hepatol. 2023;8(8):726-734.  https://doi.org/10.1016/S2468-1253(23)00104-8
  7. Makar J, Abdelmalak J, Con D, Hafeez B, Garg M. Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis. Gastrointest Endosc. 2025;101(1):68-81.e8.  https://doi.org/10.1016/j.gie.2024.08.033
  8. Lu Z, Xu Y, Yao L, Zhou W, Gong W, Yang G, Guo M, Zhang B, Huang X, He C, Zhou R, Deng Y, Yu H. Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video). Gastrointest Endosc. 2022;95(6):1186-1194.e3.  https://doi.org/10.1016/j.gie.2021.11.049
  9. Patent RF na izobretenie №2020666086/04.12.2020. Byul. №5. Khryashchev VV, Zavyalov DV, Kashin SV, Ivanovskii LI, Lebedev AA. ColonoscopyVideoAnalysis — program for differentiating the depth of tumor invasion in oncological diagnostics based on artificial intelligence methods. Ssylka aktivna na 16.12.2025. (In Russ.). https://www1.fips.ru/registers-doc-view/fips_servlet?DB=EVM&rn=2544&DocNumber=2020666086&TypeFile=html
  10. Achkasov SI, Shelygin YuA, Shabunin AV, Korzheva IYu, Fedorov E.D., Kamaletdinova YuY, Shakhmatov DG, Likutov AA, Nazarov IV, Gorbachev EV, Mingazov AF. The effectiveness of the ArtInCol artificial intelligence system in the diagnosis of neoplasms during colonoscopy: results of a multicenter randomized trial. Koloproktologia. 2025;24(3):12-21. (In Russ.). https://doi.org/10.33878/2073-7556-2025-24-3-12-21
  11. Zavyalov DV, Kashin SV, Guseinova SR. CAD EYE for real-time detection and differentiation of colorectal lesions. Russian Journal of Evidence-Based Gastroenterology. 2024;13(2):50-54. (In Russ.). https://doi.org/10.17116/dokgastro20241302150
  12. Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, Ohnishi T, Fujishiro M, Matsuo K, Fujisaki J, Tada T. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer. 2018;21(4):653-660.  https://doi.org/10.1007/s10120-018-0793-2
  13. He X, Wu L, Dong Z, Gong D, Jiang X, Zhang H, Ai Y, Tong Q, Lv P, Lu B, Wu Q, Yuan J, Xu M, Yu H. Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos). Gastrointest Endosc. 2022;95(4):671-678.e4.  https://doi.org/10.1016/j.gie.2021.11.040
  14. Luo H, Xu G, Li C, He L, Luo L, Wang Z, Jing B, Deng Y, Jin Y, Li Y, Li B, Tan W, He C, Seeruttun SR, Wu Q, Huang J, Huang DW, Chen B, Lin SB, Chen QM, Yuan CM, Chen HX, Pu HY, Zhou F, He Y, Xu RH. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol. 2019;20(12):1645-1654. https://doi.org/10.1016/S1470-2045(19)30637-0
  15. Wu L, Xu M, Jiang X, He X, Zhang H, Ai Y, Tong Q, Lv P, Lu B, Guo M, Huang M, Ye L, Shen L, Yu H. Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos). Gastrointest Endosc. 2022;95(2):269-280.e6.  https://doi.org/10.1016/j.gie.2021.09.017
  16. Dong Z, Wu L, Du H, Tao X, Wang J, Zeng X, Zhu Y, Tan S, Wang J, Deng M, Yang T, Yu L, Yu H. Artificial intelligence as a surrogate for improving detection and standardization in upper endoscopy: a multicenter randomized controlled trial. Lancet Reg Health West Pac. 2025;55S1:101308. https://doi.org/10.1016/j.lanwpc.2024.101308
  17. Nagao S, Tsuji Y, Sakaguchi Y, Takahashi Y, Minatsuki C, Niimi K, Yamashita H, Yamamichi N, Seto Y, Tada T, Koike K. Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging. Gastrointest Endosc. 2020;92(4):866-873.e1.  https://doi.org/10.1016/j.gie.2020.06.047
  18. Kim JH, Oh SI, Han SY, Keum JS, Kim KN, Chun JY, Youn YH, Park H. An Optimal Artificial Intelligence System for Real-Time Endoscopic Prediction of Invasion Depth in Early Gastric Cancer. Cancers (Basel). 2022;14(23):6000. https://doi.org/10.3390/cancers14236000
  19. Zhang L, Luo R, Tang D, Zhang J, Su Y, Mao X, Ye L, Yao L, Zhou W, Zhou J, Lu Z, Zhang M, Xu Y, Deng Y, Huang X, He C, Xiao Y, Wang J, Wu L, Li J, Zou X, Yu H. Human-Like Artificial Intelligent System for Predicting Invasion Depth of Esophageal Squamous Cell Carcinoma Using Magnifying Narrow-Band Imaging Endoscopy: A Retrospective Multicenter Study. Clin Transl Gastroenterol. 2023;14(10):e00606. https://doi.org/10.14309/ctg.0000000000000606
  20. Ling T, Wu L, Fu Y, Xu Q, An P, Zhang J, Hu S, Chen Y, He X, Wang J, Chen X, Zhou J, Xu Y, Zou X, Yu H. A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy. Endoscopy. 2021;53(5):469-477.  https://doi.org/10.1055/a-1229-0920
  21. An P, Yang D, Wang J, Wu L, Zhou J, Zeng Z, Huang X, Xiao Y, Hu S, Chen Y, Yao F, Guo M, Wu Q, Yang Y, Yu H. A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy. Gastric Cancer. 2020;23(5):884-892.  https://doi.org/10.1007/s10120-020-01071-7
  22. Li B, Dong YL, Liu JY, Tan WM, He DL, Qi ZP, Yu HH, Shi Q, Ren Z, Cai MY, Cai SL, Yan B, Zhong YS. Application of artificial intelligence lesion labeling system-assisted endoscopic submucosal dissection for the treatment of esophageal lesions in a low-volume center: a prospective cohort study. Int J Surg. 2025;111(9):6093-6101. https://doi.org/10.1097/JS9.0000000000002748
  23. Li N, Yang J, Li X, Shi Y, Wang K. Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis. PLoS One. 2024;19(5):e0303421. https://doi.org/10.1371/journal.pone.0303421
  24. Tao X, Zhu Y, Dong Z, Huang L, Shang R, Du H, Wang J, Zeng X, Wang W, Wang J, Li Y, Deng Y, Wu L, Yu H. An artificial intelligence system for chronic atrophic gastritis diagnosis and risk stratification under white light endoscopy. Dig Liver Dis. 2024;56(8):1319-1326. https://doi.org/10.1016/j.dld.2024.01.177
  25. Zhang M, Pan J, Lin J, Xu M, Zhang L, Shang R, Yao L, Li Y, Zhou W, Deng Y, Dong Z, Zhu Y, Tao X, Wu L, Yu H. An explainable artificial intelligence system for diagnosing Helicobacter Pylori infection under endoscopy: a case-control study. Therap Adv Gastroenterol. 2023;16:17562848231155023. https://doi.org/10.1177/17562848231155023
  26. Li J, Hu S, Shi C, Dong Z, Pan J, Ai Y, Liu J, Zhou W, Deng Y, Li Y, Yuan J, Zeng Z, Wu L, Yu H. A deep learning and natural language processing-based system for automatic identification and surveillance of high-risk patients undergoing upper endoscopy: A multicenter study. Eur Clin Med. 2022;53:101704. https://doi.org/10.1016/j.eclinm.2022.101704
  27. Tan X, Yao L, Dong Z, Li Y, Yu Y, Gao X, Zhu K, Su W, Yin H, Wang W, Luo C, Li J, You H, Hu H, Zhou W, Yu H.Artificial Intelligence as a Surrogate for Inspection Time to Assess Completeness in Esophagogastroduodenoscopy: A Prospective, Randomized, Noninferiority Study. Clin Transl Gastroenterol. 2025;16(6):e00839. https://doi.org/10.14309/ctg.0000000000000839
  28. Ahn BY, Lee J, Seol J, Kim JY, Chung H. Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy. Sci Rep. 2025;15:4693. https://doi.org/10.1038/s41598-024-83721-9
  29. Xu C, Zhu Y, Wu L, Yu H, Liu J, Zhou F, Xiong Q, Wang S, Cui S, Huang X, Yin A, Xu T, Lei S, Xia Z. Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial. BMC Anesthesiol. 2022;22(1):313.  https://doi.org/10.1186/s12871-022-01796-1
  30. Kim SH, Lim YJ. Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges. Diagnostics (Basel). 2021;11(9):1722. https://doi.org/10.3390/diagnostics11091722
  31. Dhali A, Kipkorir V, Maity R, Srichawla BS, Biswas J, Rathna RB, Bharadwaj HR, Ongidi I, Chaudhry T, Morara G, Waithaka M, Rugut C, Lemashon M, Cheruiyot I, Ojuka D, Ray S, Dhali GK. Artificial Intelligence-Assisted Capsule Endoscopy Versus Conventional Capsule Endoscopy for Detection of Small Bowel Lesions: A Systematic Review and Meta-Analysis. J Gastroenterol Hepatol. 2025;40(5):1105-1118. https://doi.org/10.1111/jgh.16931
  32. Qin K, Li J, Fang Y, Xu Y, Wu J, Zhang H, Li H, Liu S, Li Q. Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis. Surg Endosc. 2022;36(1):16-31.  https://doi.org/10.1007/s00464-021-08689-3
  33. Bang CS, Lee JJ, Baik GH. Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis. J Med Internet Res. 2021;23(12):e33267. https://doi.org/10.2196/33267
  34. Mohan BP, Khan SR, Kassab LL, Ponnada S, Chandan S, Ali T, Dulai PS, Adler DG, Kochhar GS. High pooled performance of convolutional neural networks in computer-aided diagnosis of GI ulcers and/or hemorrhage on wireless capsule endoscopy images: a systematic review and meta-analysis. Gastrointest Endosc. 2021;93(2):356-364.e4.  https://doi.org/10.1016/j.gie.2020.07.038
  35. O‘Hara FJ, Mc Namara D. Capsule endoscopy with artificial intelligence-assisted technology: Real-world usage of a validated AI model for capsule image review. Endosc Int Open. 2023;11(10):E970-E975. https://doi.org/10.1055/a-2161-1816
Contact the author
Email
Message
The publishing house "Media Sphere" does not provide treatment services, does not give recommendations on contacting medical institutions and specific specialists, on the prescription of medicines and dietary supplements.

Email Confirmation

An email was sent to test@gmail.com with a confirmation link. Follow the link from the letter to complete the registration on the site.

Email Confirmation

Contact us
If you have any questions, complaints or suggestions, please use this feedback form.
Email
Message
The publishing house "Media Sphere" does not provide treatment services, does not give recommendations on contacting medical institutions and specific specialists, on the prescription of medicines and dietary supplements.
Submit Application

You can become an author of one of our magazines, send a request and we will consider it in during the day.

Name
Phone
E-mail
Message
Login
Registration
E-mail
Password
Forgot Password?

Log in to the site using your account in one of the services

Password recovery
E-mail
Login
Register

We use cооkies to improve the performance of the site. By staying on our site, you agree to the terms of use of cооkies. To view our Privacy and Cookie Policy, please. click here.