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
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Gastroenterology & Hepatology
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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 21.7 Hours
Abstract
BACKGROUND
Artificial intelligence (AI) in gastrointestinal endoscopy has focused on computer-aided detection (CADe) for recognition errors; AI addressing endoscope motion, coverage, workflow, and skill – collectively kinematic analysis – has not been systematically synthesized.
AIM
To map kinematic AI evidence across eight predefined domains and assessed its quality relative to CADe and surgical AI.
METHODS
MEDLINE/PubMed, EMBASE-OVID, Cochrane, and IEEE Xplore were searched following Preferred Reporting Items for Systematic reviews and Meta-Analyses checklist (CRD420261320279). Studies applying AI to kinematic data in gastrointestinal endoscopy were eligible. Risk of bias used Cochrane Risk of Bias 2, Quality Assessment of Diagnostic Accuracy Studies-2, and PROBAST + AI; certainty of evidence used GRADE.
RESULTS
Fifty-eight studies were included. Evidence generation was asymmetric: (1) Randomized controlled trial ratio approximately 5:1 (> 40 vs 8); (2) Patient ratio approximately 4:1 (> 27000 vs approximately 6200); (3) Annual publication ratio approximately 12:1; and (4) ≥ 6 vs 0 regulatory approvals favoring diagnostic AI. Withdrawal speed monitoring and coverage/blind-spot mapping reached moderate GRADE certainty, but all 8 randomized controlled trials were from China and 6 of 8 used the ENDOANGEL platform. The remaining six domains have not reached moderate certainty: (1) Workflow recognition and skill assessment are approaching clinical readiness; and (2) Autonomous navigation, robotic control, simultaneous localization and mapping/three-dimensional reconstruction, and instrument tracking remain at the engineering stage. Comparison with surgical AI indicated that the gap reflects ecosystem-level factors – absence of open datasets, benchmarking challenges, and regulatory pathways for motion-acting AI – rather than technical immaturity.
CONCLUSION
Kinematic AI addresses exposure errors that CADe cannot correct and has shown additive benefit alongside CADe. Two domains are ready for geographically diverse validation; six remain at the engineering stage. Priorities are open datasets, benchmarking infrastructure, and dedicated regulatory pathways for motion-acting AI.
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.
