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DOI: 10.1055/a-2742-4342
Curriculum for safe and effective use of artificial intelligence in endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement
Autor*innen
Abstract
The European Society of Gastrointestinal Endoscopy (ESGE) has identified a critical need to establish structured training for safe and effective use of artificial intelligence (AI) in endoscopy. This manuscript presents the results of a formal Delphi consensus process and outlines the official ESGE position, offering a comprehensive curriculum for acquiring and maintaining the competence needed to exploit the benefit of using AI tools in endoscopy. The proposed framework defines the prerequisites in the preadoption phase, core training components, and requirements to maintain optimal implementation. Key recommendations include: (1) ensuring basic competency in standard endoscopy procedures; (2) acquiring foundational knowledge of AI principles; (3) implementing educational programs to enhance AI literacy; (4) recognizing and mitigating cognitive biases in human–AI interaction; (5) avoiding over-reliance on AI in clinical decision-making; and (6) continuous monitoring of key performance indicators throughout AI system integration.
Publikationsverlauf
Artikel online veröffentlicht:
03. Dezember 2025
© 2025. © 2025. European Society of Gastrointestinal Endoscopy. All rights reserved..
Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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References
- 1 Hassan C, Bisschops R, Sharma P. et al. Colon cancer screening, surveillance, and treatment: novel artificial intelligence driving strategies in the management of colon lesions. Gastroenterology 2025; 169: 444-455
- 2 Halvorsen N, Hassan C, Correale L. et al. Benefits, burden, and harms of computer aided polyp detection with artificial intelligence in colorectal cancer screening: microsimulation modelling study. BMJ Med 2025; 4: e001446
- 3 Budzyń K, Romańczyk M, Kitala D. et al. Endoscopist deskilling after exposure to artificial intelligence in colonoscopy: a multicentre, observational study. Lancet Gastroenterol Hepatol 2025; 10: 896-903
- 4 Halvorsen N, Barua I, Kudo SE. et al. Leaving colorectal polyps in situ with endocytoscopy assisted by computer-aided diagnosis: a cost-effectiveness study. Endoscopy 2025; 57: 611-619
- 5 Areia M, Mori Y, Correale L. et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. Lancet Digit Health 2022; 4: e436-e444
- 6 Bisschops R, Dekker E, East JE. et al. European Society of Gastrointestinal Endoscopy (ESGE) curricula development for postgraduate training in advanced endoscopic procedures: rationale and methodology. Endoscopy 2019; 51: 976-979
- 7 Guyatt GH, Oxman AD, Vist GE. et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 2008; 336: 924-926
- 8 Hsu C-C, Sandford BA. The Delphi technique: making sense of consensus. Pract Assess Res Eval 2007; 12: 10
- 9 Antonelli G, Voiosu AM, Pawlak KM. et al. Training in basic gastrointestinal endoscopic procedures: a European Society of Gastrointestinal Endoscopy (ESGE) and European Society of Gastroenterology and Endoscopy Nurses and Associates (ESGENA) Position Statement. Endoscopy 2024; 56: 131-150
- 10 Jin EH, Lee D, Bae JH. et al. Improved accuracy in optical diagnosis of colorectal polyps using convolutional neural networks with visual explanations. Gastroenterology 2020; 158: 2169-2179 e2168
- 11 Hassan C, Wallace MB, Sharma P. et al. New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection. Gut 2020; 69: 799-800
- 12 Rex DK, Bhavsar-Burke I, Buckles D. et al. Artificial intelligence for real-time prediction of the histology of colorectal polyps by general endoscopists. Ann Intern Med 2024; 177: 911-918
- 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
- 14 Meinikheim M, Mendel R, Palm C. et al. Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett's esophagus: a tandem randomized and video trial. Endoscopy 2024; 56: 641-649
- 15 Yamaguchi D, Shimoda R, Miyahara K. et al. Impact of an artificial intelligence-aided endoscopic diagnosis system on improving endoscopy quality for trainees in colonoscopy: prospective, randomized, multicenter study. Dig Endosc 2024; 36: 40-48
- 16 An P, Wang Z. Application value of an artificial intelligence-based diagnosis and recognition system in gastroscopy training for graduate students in gastroenterology: a preliminary study. Wien Med Wochenschr 2024; 174: 173-180
- 17 Li YD, Zhu SW, Yu JP. et al. Intelligent detection endoscopic assistant: An artificial intelligence-based system for monitoring blind spots during esophagogastroduodenoscopy in real-time. Dig Liver Dis 2021; 53: 216-223
- 18 Yao L, Liu J, Wu L. et al. A gastrointestinal endoscopy quality control system incorporated with deep learning improved endoscopist performance in a pretest and post-test trial. Clin Transl Gastroenterol 2021; 12: e00366
- 19 Byrne MF, Chapados N, Soudan F. et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 2019; 68: 94-100
- 20 van der Sommen F, de Groof J, Struyvenberg M. et al. Machine learning in GI endoscopy: practical guidance in how to interpret a novel field. Gut 2020; 69: 2035-2045
- 21 Mori Y, Jin EH, Lee D. Enhancing artificial intelligence–doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy. Dig Liver Dis 2024; 56: 1140-1143
- 22 Rodrigues T, Keswani R. Endoscopy training in the age of artificial intelligence: deep learning or artificial competence?. Clin Gastroenterol Hepatol 2023; 21: 8-10
- 23 Tham S, Koh FH, Teo EK. et al. Knowledge, perceptions and behaviours of endoscopists towards the use of artificial intelligence-aided colonoscopy. Surg Endosc 2023; 37: 7395-7400
- 24 Hassan C, Rizkala T, Mori Y. et al. Computer-aided diagnosis for the resect-and-discard strategy for colorectal polyps: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol 2024; 9: 1010-1019
- 25 van der Zander QEW, Roumans R, Kusters CHJ. et al. Appropriate trust in artificial intelligence for the optical diagnosis of colorectal polyps: the role of human/artificial intelligence interaction. Gastrointest Endosc 2024; 100: 1070-1078.e1010
- 26 Goddard K, Roudsari A, Wyatt JC. Automation bias: a systematic review of frequency, effect mediators, and mitigators. J Am Med Inform Assoc 2012; 19: 121-127
- 27 Campion JR, O'Connor DB, Lahiff C. Human-artificial intelligence interaction in gastrointestinal endoscopy. World J Gastrointest Endosc 2024; 16: 126-135
- 28 Wickens CD, Clegg BA, Vieane AZ. et al. Complacency and automation bias in the use of imperfect automation. Hum Factors 2015; 57: 728-739
- 29 Lyell D, Coiera E. Automation bias and verification complexity: a systematic review. J Am Med Inform Assoc 2017; 24: 423-431
- 30
Sujan M,
Furniss D,
Hawkins R.
et al.
Human factors of using artificial intelligence in healthcare: challenges that stretch
across industries. Proceedings of the 28th Safety-Critical Systems Symposium 11–13
February 2020; York, UK.
- 31 Lieder F, Griffiths TL, Huys QJM. et al. The anchoring bias reflects rational use of cognitive resources. Psychon Bull Rev 2018; 25: 322-349
- 32 Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases. Science 1974; 185: 1124-1131
- 33 Rastogi C, Zhang Y, Wei D. et al. Deciding fast and slow: the role of cognitive biases in AI-assisted decision-making. In: Proceedings of the ACM on Human-Computer Interaction; 30 April – 5 May 2022; New Orleans, LA, USA. New York: Association for Computing Machinery; 2022; 6: 1-22
- 34 Castelo N, Bos MW, Lehmann DR. Task-dependent algorithm aversion. J Mark Res 2019; 56: 809-825
- 35 Dietvorst BJ, Simmons JP, Massey C. Algorithm aversion: people erroneously avoid algorithms after seeing them err. J Exp Psychol Gen 2015; 144: 114-126
- 36 Htet H, Siggens K, Saiko M. et al. Importance of human-machine interaction in detection of Barrett’s neoplasia using a novel deep neural network in the evolving era of artificial intelligence. Gastrointest Endosc 2023; 97: AB771
- 37 Burton J, Stein M-K, Blegind Jensen T. A systematic review of algorithm aversion in augmented decision making. J Behav Decis Mak 2019; 33: 220-239
- 38 Cherubini A. Human–artificial intelligence collaboration: insights and lessons from colonoscopy artificial intelligence integration. AI in Precision Oncology 2024; 1: 179-183
- 39 Dix A. Human–computer interaction, foundations and new paradigms. J Vis Lang Comput 2017; 42: 122-134
- 40 Reverberi C, Rigon T, Solari A. et al. Experimental evidence of effective human-AI collaboration in medical decision-making. Sci Rep 2022; 12: 14952
- 41 Taghiakbari M, Rex DK, Pohl H. et al. Implementing discard strategies for diminutive polyps using autonomous CADx in clinical practice. Gut 2025;
- 42 Soleymanjahi S, Huebner J, Elmansy L. et al. Artificial intelligence-assisted colonoscopy for polyp detection: a systematic review and meta-analysis. Ann Intern Med 2024; 177: 1652-1663
- 43 Spadaccini M, Iannone A, Maselli R. et al. Computer-aided detection versus advanced imaging for detection of colorectal neoplasia: a systematic review and network meta-analysis. Lancet Gastroenterol Hepatol 2021; 6: 793-802
- 44 Li SW, Zhang LH, Cai Y. et al. Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study. Sci Transl Med 2024; 16: eadk5395
- 45 Yuan XL, Liu W, Lin YX. et al. Effect of an artificial intelligence-assisted system on endoscopic diagnosis of superficial oesophageal squamous cell carcinoma and precancerous lesions: a multicentre, tandem, double-blind, randomised controlled trial. Lancet Gastroenterol Hepatol 2024; 9: 34-44
- 46 Makar J, Abdelmalak J, Con D. et al. Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis. Gastrointest Endosc 2025; 101: 68-81.e68
- 47 Wu L, Shang R, Sharma P. et al. Effect of a deep learning-based system on the miss rate of gastric neoplasms during upper gastrointestinal endoscopy: a single-centre, tandem, randomised controlled trial. Lancet Gastroenterol Hepatol 2021; 6: 700-708
- 48 Seager A, Sharp L, Neilson LJ. et al. Polyp detection with colonoscopy assisted by the GI Genius artificial intelligence endoscopy module compared with standard colonoscopy in routine colonoscopy practice (COLO-DETECT): a multicentre, open-label, parallel-arm, pragmatic randomised controlled trial. Lancet Gastroenterol Hepatol 2024; 9: 911-923
- 49 Karsenti D, Tharsis G, Perrot B. et al. 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: 726-734
- 50 Patel HK, Mori Y, Hassan C. et al. Lack of effectiveness of computer aided detection for colorectal neoplasia: a systematic review and meta-analysis of nonrandomized studies. Clin Gastroenterol Hepatol 2024; 22: 971-980.e15
- 51 Troya J, Fitting D, Brand M. et al. The influence of computer-aided polyp detection systems on reaction time for polyp detection and eye gaze. Endoscopy 2022; 54: 1009-1014
