Gong EJ, Bang CS, Lee JJ. Artificial intelligence for kinematic (procedural motion) analysis in gastrointestinal endoscopy: A systematic review. World J Gastroenterol 2026; 32(41): 122556 [DOI: 10.3748/wjg.122556]
Corresponding Author of This Article
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
Research Domain of This Article
Gastroenterology & Hepatology
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research-article
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Gong EJ, Bang CS, Lee JJ. Artificial intelligence for kinematic (procedural motion) analysis in gastrointestinal endoscopy: A systematic review. World J Gastroenterol 2026; 32(41): 122556 [DOI: 10.3748/wjg.122556]
World J Gastroenterol. Nov 7, 2026; 32(41): 122556 Published online Nov 7, 2026. doi: 10.3748/wjg.122556
Artificial intelligence for kinematic (procedural motion) analysis in gastrointestinal endoscopy: A systematic review
Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee
Eun Jeong Gong, Chang Seok Bang, Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Gangwon-do, South Korea
Jae Jun Lee, Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Gangwon-do, South Korea
Co-corresponding authors: Chang Seok Bang and Jae Jun Lee.
Author contributions: Gong EJ and Bang CS were responsible for writing-original draft; Bang CS was responsible for conceptualization, formal analysis, methodology, project administration, resources; Bang CS and Lee JJ were responsible for writing-review and editing as co-corresponding authors; Lee JJ was responsible for funding acquisition; Gong EJ, Bang CS, and Lee JJ were responsible for data curation, investigation; all of the authors read and approved the final version of the manuscript to be published.
AI contribution statement: We did not use AI in the preparation of this manuscript.
Supported by the Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT), No. RS-2023-00223501.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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
Received: April 22, 2026 Revised: May 19, 2026 Accepted: June 24, 2026 Published online: November 7, 2026 Processing time: 145 Days and 21.1 Hours
Core Tip
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.
