| ISSN |
2644-3236 (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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Review |
| Article Title |
Multimodal artificial intelligence integrates imaging, endoscopic, and omics data for intelligent decision-making in individualized gastrointestinal tumor treatment
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Hui Nian, Yi-Bin Wu, Yu Bai, Zhi-Long Zhang, Xiao-Huang Tu, Qi-Zhi Liu, De-Hua Zhou and Qian-Cheng Du |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Xuhui District Health Commission |
SHXH202214 |
|
| Corresponding Author |
Qian-Cheng Du, MD, Department of Thoracic Surgery, Shanghai Xuhui Central Hospital, No. 366 Longchuan North Road, Xuhui District, Shanghai 200031, China. duqc1991106@sina.com |
| Key Words |
Multimodal artificial intelligence; Gastrointestinal tumors; Individualized therapy; Intelligent diagnosis; Treatment optimization; Prognostic prediction; Data fusion; Deep learning; Precision medicine |
| Core Tip |
This review highlights that multimodal artificial intelligence (AI), by integrating imaging, endoscopic, and multi-omics data, is revolutionizing the intelligent decision-making process for individualized gastrointestinal tumor therapy. It enhances precision across the entire clinical spectrum-from improving early detection and accurate staging, to optimizing treatment planning and prognostic assessment. The key to its success lies in effectively addressing challenges related to data fusion, model interpretability, and multicenter validation. Ultimately, multimodal AI serves as a pivotal translational bridge, connecting complex data analysis with actionable clinical insights to advance precision oncology. |
| Publish Date |
2026-01-06 08:29 |
| Citation |
Nian H, Wu YB, Bai Y, Zhang ZL, Tu XH, Liu QZ, Zhou DH, Du QC. Multimodal artificial intelligence integrates imaging, endoscopic, and omics data for intelligent decision-making in individualized gastrointestinal tumor treatment. Artif Intell Gastroenterol 2026; 7(1): 115498 |
| URL |
https://www.wjgnet.com/2644-3236/full/v7/i1/115498.htm |
| DOI |
https://dx.doi.org/10.35712/aig.v7.i1.115498 |
