Publication Name	World Journal of Gastrointestinal Endoscopy
Manuscript ID	126153
DOI	10.4253/wjge.126153
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Minireviews
Article Title	Multimodal artificial intelligence and large language models in gastrointestinal endoscopy: From lesion detection to workflow-integrated report generation
Manuscript Source	Invited Manuscript
All Author List	Hua-Wei He, In Neng Chan, Ye-Ying Qin, Tao Yan and Pak Kin Wong
Funding Agency and Grant Number	Funding Agency Grant Number Guangdong Basic and Applied Basic Research Foundation 2021B1515130003 Key Research and Development Plan of Hubei Province 2022BCE034 Natural Science Foundation of Hubei Province 2024AFB1054
Corresponding Author	Pak Kin Wong, PhD, Professor, Department of Electromechanical Engineering, University of Macau, Avenida da Universidade, Taipa, Macau 999078, China. fstpkw@um.edu.mo
Key Words	Gastrointestinal endoscopy; Artificial intelligence; Multimodal learning; Large language models; Automated report generation; Computer-aided diagnosis
Core Tip	Artificial intelligence in gastrointestinal endoscopy is evolving from single lesion identification and diagnosis to multimodal, workflow-oriented support. The review explores methods to integrate endoscopic images and videos with clinical text, histology, and procedural data using multimodal learning, vision-language models, multimodal large language models (LLMs), and complementary text-based LLMs. The maturity of the clinical evidence varies greatly; however, the current evidence supports applications in lesion classification, procedural quality assessment, systematic documentation and draft report generation. Limited multimodal datasets, hallucination and grounding issues, lack of external validation, workflow integration, privacy and regulatory restrictions are still key challenges for mainstream clinical application.
Citation	He HW, Chan IN, Qin YY, Yan T, Wong PK. Multimodal artificial intelligence and large language models in gastrointestinal endoscopy: From lesion detection to workflow-integrated report generation. World J Gastrointest Endosc 2026; In press DOI: https://doi.org/10.4253/wjge.126153
PDF	126153-in-press.pdf

Received	2026-08-03 08:49
Peer-Review Started	2026-08-05 06:04
First Decision by Editorial Office Director
Return for Revision	2026-09-04 16:38
Revised	2026-09-17 10:01
Publication Fee Transferred
Second Decision by Editor	2026-10-21 02:57
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-10-21 10:09
Articles in Press	2026-10-21 10:09
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-5190 (online)
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Copyright	©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc.
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