Publication Name	Artificial Intelligence in Gastrointestinal Endoscopy
Manuscript ID	87481
Country	Australia

Received	2023-09-04 22:03
Peer-Review Started	2023-09-04 22:05
To Make the First Decision
Return for Revision	2023-11-01 08:17
Revised	2023-11-07 04:31
Second Decision	2023-11-21 01:44
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2023-11-30 06:19
Articles in Press	2023-11-30 06:19
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2023-12-01 03:44
Publish the Manuscript Online	2023-12-07 03:43

ISSN	2689-7164 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Copyright	©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
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Category	Gastroenterology & Hepatology
Manuscript Type	Retrospective Cohort Study
Article Title	Artificial intelligence fails to improve colonoscopy quality: A single centre retrospective cohort study
Manuscript Source	Unsolicited Manuscript
All Author List	Naeman Goetz, Katherine Hanigan and Richard Kai-Yuan Cheng
ORCID	Author(s) ORCID Number Naeman Goetz http://orcid.org/0000-0001-9009-5098 Richard Kai-Yuan Cheng http://orcid.org/0000-0002-7286-0099
Funding Agency and Grant Number
Corresponding Author	Naeman Goetz, BSc, MD, Doctor, Doctor, Department of Gastroenterology, Redcliffe Hospital, Anzac Ave, Redcliffe QLD 4020, Redcliffe 4020, Australia. naeman.goetz@health.qld.gov.au
Key Words	Artificial intelligence; Colonoscopy quality; Adenoma detection rate; Sessile serrated lesion detection rate; Withdrawal time
Core Tip	This paper investigates the utility of Artificial Intelligence Assisted Colonoscopy (AIAC) in enhancing colonoscopy quality, particularly adenoma detection rate. Using a retrospective design, we compare AIAC with conventional colonoscopy in a real-world setting, finding no significant improvement in surrogate markers of colonoscopy quality. We explore challenges in artificial intelligence-human interaction and emphasise the need for further validation.
Publish Date	2023-12-07 03:43
Citation	Goetz N, Hanigan K, Cheng RKY. Artificial intelligence fails to improve colonoscopy quality: A single centre retrospective cohort study. Artif Intell Gastrointest Endosc 2023; 4(2): 18-26
URL	https://www.wjgnet.com/2689-7164/full/v4/i2/18.htm
DOI	https://dx.doi.org/10.37126/aige.v4.i2.18

Full Article (PDF)	AIGE-4-18-with-cover.pdf
Full Article (Word)	AIGE-4-18.docx
STROBE Statement	87481-STROBE statement.pdf
Manuscript File	87481_Auto_Edited-LJH-JLW.docx
Answering Reviewers	87481-Answering reviewers.pdf
Audio Core Tip	87481-Audio core tip.m4a
Biostatistics Review Certificate	87481-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form	87481-Conflict-of-interest statement.pdf
Copyright License Agreement	87481-Copyright license agreement.pdf
Signed Informed Consent Form(s) or Document(s)	87481-Informed consent statement.pdf
Institutional Review Board Approval Form or Document	87481-Institutional review board statement.pdf
Peer-review Report	87481-Peer-review(s).pdf
Scientific Misconduct Check	87481-CrossCheck.png
Scientific Misconduct Check	87481-Bing-Liu JH-2.png
Scientific Editor Work List	87481-Scientific editor work list.pdf
CrossCheck Report	87481-CrossCheck report.pdf