Publication Name	World Journal of Gastroenterology
Manuscript ID	122556
DOI	10.3748/wjg.122556
Country	South Korea

Category	Gastroenterology & Hepatology
Manuscript Type	Systematic Reviews
Article Title	Artificial intelligence for kinematic (procedural motion) analysis in gastrointestinal endoscopy: A systematic review
Manuscript Source	Unsolicited Manuscript
All Author List	Eun Jeong Gong, Chang Seok Bang and Jae Jun Lee
Funding Agency and Grant Number	Funding Agency Grant Number the Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) No. RS-2023-00223501
Corresponding Author	Chang Seok Bang, MD, PhD, Department of Internal Medicine, Hallym University College of Medicine, Sakju-ro 77, Chuncheon 24253, Gangwon-do, South Korea. cloudslove@naver.com
Key Words	Artificial intelligence; Kinematics; Endoscopy; Exposure error; Quality monitoring
Core Tip	Artificial intelligence (AI) in gastrointestinal (GI) endoscopy has been dominated by computer-aided detection (CADe) of lesions – targeting recognition errors – with over 40 randomized controlled trials (RCTs) and multiple regulatory approvals, whereas AI addressing endoscope motion, coverage, and procedural workflow has developed along a separate, largely engineering-focused trajectory. No prior systematic review has mapped the distribution, translational maturity, and certainty of evidence for kinematic AI across GI endoscopy domains. Compared with diagnostic AI, kinematic AI has produced approximately one-fifth the number of RCTs, one-quarter the number of enrolled patients, about one-twelfth the annual publication output, and no standalone regulatory approvals. Only two of eight domains reached moderate GRADE certainty; all eight RCTs were conducted in Chinese centers and six used the ENDOANGEL platform, indicating pronounced geographic and platform concentration. The remaining six domains are at the pre-clinical/engineering stage; the gap with surgical AI is best explained by ecosystem-level factors rather than by technical immaturity alone. The four-arm RCT demonstrated that computer-aided quality and CADe address independent failure modes with additive benefit on adenoma detection rate, supporting integration of kinematic monitoring into existing CADe platforms as the most direct translational pathway. Priority investments include building open kinematic datasets, establishing GI-specific benchmarking challenges analogous to the EndoVis series, conducting colonoscopy three-dimensional coverage RCTs outside China, and defining regulatory pathways for AI that acts on motion rather than on images.
Citation	Gong EJ, Bang CS, Lee JJ. Artificial intelligence for kinematic (procedural motion) analysis in gastrointestinal endoscopy: A systematic review. World J Gastroenterol 2026; In press DOI: https://doi.org/10.3748/wjg.122556
PDF	122556-in-press.pdf

Received	2026-04-22 02:16
Peer-Review Started	2026-04-22 02:18
First Decision by Editorial Office Director	2026-05-13 07:01
Return for Revision	2026-05-13 07:01
Revised	2026-05-19 13:43
Publication Fee Transferred	2026-05-24 13:26
Second Decision by Editor	2026-06-24 02:41
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-06-24 06:28
Articles in Press	2026-06-24 06:28
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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