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CC BY 4.0 · Endosc Int Open 2025; 13: a26047345
DOI: 10.1055/a-2604-7345
DOI: 10.1055/a-2604-7345
Editorial
Between hype and hard evidence: Are large language models ready for implementation in surveillance colonoscopy?

Keywords
Endoscopy Lower GI Tract - Polyps/adenomas/... - Colorectal cancer - Quality and logistical aspects - Quality managementPublication History
Received: 19 March 2025
Accepted: 06 May 2025
Article published online:
17 June 2025
© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).
Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
Bibliographical Record
Marco Bustamante-Balén. Between hype and hard evidence: Are large language models ready for implementation in surveillance colonoscopy?. Endosc Int Open 2025; 13: a26047345.
DOI: 10.1055/a-2604-7345
Marco Bustamante-Balén. Between hype and hard evidence: Are large language models ready for implementation in surveillance colonoscopy?. Endosc Int Open 2025; 13: a26047345.
DOI: 10.1055/a-2604-7345
-
References
- 1 American Cancer Society (2025). Key Statistics for Colorectal Cancer. https://www.cancer.org/cancer/types/colon-rectal-cancer/about/key-statistics.html
MissingFormLabel
- 2
Siegel RL,
Wagle NS,
Cercek A.
et al.
Colorectal cancer statistics, 2023. CA Cancer J Clin 2023; 73: 233-254
MissingFormLabel
- 3
Lieberman DA,
Williams JL,
Holub JL.
et al.
Colonoscopy utilization and outcomes 2000 to 2011. Gastrointest Endosc 2014; 80: 133-143.e133
MissingFormLabel
- 4
Patel N,
Tong L,
Ahn C.
et al.
Post-polypectomy guideline adherence: Importance of belief in guidelines, not guideline
knowledge or fear of missed cancer. Digest Dis Sci 2015; 60: 2937-2945
MissingFormLabel
- 5
Radaelli F,
Paggi S,
Bortoli A.
et al.
Overutilization of post-polypectomy surveillance colonoscopy in clinical practice:
a prospective, multicentre study. Dig Liver Dis 2012; 44: 748-753
MissingFormLabel
- 6
Amini M.
Comparing ChatGPT3.5 and Bard in recommending colonoscopy intervals: Bridging the
gap in healthcare settings. Endosc Int Open 2025;
MissingFormLabel
- 7
Shool S,
Adimi S,
Saboori Amleshi R.
et al.
A systematic review of large language model (LLM) evaluations in clinical medicine.
BMC Med Inform Decis Mak 2025; 25: 117
MissingFormLabel
- 8
Tariq R,
Malik S,
Khanna S.
Evolving landscape of large language models: An evaluation of ChatGPT and Bard in
answering patient queries on colonoscopy. Gastroenterology 2024; 166: 220-221
MissingFormLabel
- 9 Nori HK N, McKinney SM, Carignan D et al. Capabilities of GPT-4 on medical challenge
problems. arXiv preprint arXiv:2303.13375v2.2023. https://arXiv.org/abs/2303.13375
MissingFormLabel
- 10
Ghersin I,
Weisshof R,
Koifman E.
et al.
Comparative evaluation of a language model and human specialists in the application
of European guidelines for the management of inflammatory bowel diseases and malignancies.
Endoscopy 2024; 56: 706-709
MissingFormLabel
- 11
Wang L,
Chen X,
Deng X.
et al.
Prompt engineering in consistency and reliability with the evidence-based guideline
for LLMs. NPJ Digit Med 2024; 7: 41
MissingFormLabel
- 12
Tang Y,
Xiao Z,
Li X.
et al.
Large language model in medical information extraction from titles and abstracts
with prompt engineering strategies: A comparative study of GPT-3.5 and GPT-4. medRxiv
2025;
MissingFormLabel
- 13
Sauvage E,
Campano S,
Ouali L.
et al.
Does the structure of textual content have an impact on language models for automatic
summarization?. Bangkok, Thailand: Association for Computational Linguistics; 2024
MissingFormLabel