Suri C, Ratre YK, Pande B, Bhaskar L, Verma HK. Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine. World J Gastroenterol 2026; 32(1): 111428 [DOI: 10.3748/wjg.v32.i1.111428]
Corresponding Author of This Article
Henu K Verma, PhD, Assistant Professor, Department of Bioscience and Biomedical Engineering Indian Institute of Technology, Kutelabhata, Bhilai 491002, Chhattisgarh, India. henu.verma@yahoo.com
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Gastroenterology & Hepatology
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Jan 7, 2026 (publication date) through Jan 12, 2026
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World Journal of Gastroenterology
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1007-9327
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Suri C, Ratre YK, Pande B, Bhaskar L, Verma HK. Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine. World J Gastroenterol 2026; 32(1): 111428 [DOI: 10.3748/wjg.v32.i1.111428]
World J Gastroenterol. Jan 7, 2026; 32(1): 111428 Published online Jan 7, 2026. doi: 10.3748/wjg.v32.i1.111428
Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine
Chahat Suri, Yashwant K Ratre, Babita Pande, LVKS Bhaskar, Henu K Verma
Chahat Suri, Department of Oncology, University of Alberta, Edmonton T6G 2R3, Alberta, Canada
Yashwant K Ratre, Department of Biotechnology, Guru Ghasidas Vishwavidyalaya, Bilaspur 495001, Chhattisgarh, India
Babita Pande, School of Studies in Life Science, Pt. Ravishankar Shukla University, Raipur 492010, Chhattisgarh, India
LVKS Bhaskar, Department of Zoology, Guru Ghasidas Vishwavidyalaya, Bilaspur 495001, Chhattisgarh, India
Henu K Verma, Department of Bioscience and Biomedical Engineering Indian Institute of Technology, Bhilai 491002, Chhattisgarh, India
Co-first authors: Chahat Suri and Yashwant K Ratre.
Author contributions: Verma HK lead the study; Suri C, Ratre YK were involved in the data collection and validation, provided the first draft of the manuscript; Ratre YK and Pande B prepared the figures and tables; Suri C, Ratre YK, Verma HK and Bhaskar L wrote and finalized the manuscript; Verma HK and Bhaskar L designed the outline and coordinated the writing of the paper; all authors have read and agreed to the published version of the manuscript.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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/
Corresponding author: Henu K Verma, PhD, Assistant Professor, Department of Bioscience and Biomedical Engineering Indian Institute of Technology, Kutelabhata, Bhilai 491002, Chhattisgarh, India. henu.verma@yahoo.com
Received: July 10, 2025 Revised: August 23, 2025 Accepted: November 20, 2025 Published online: January 7, 2026 Processing time: 190 Days and 6.6 Hours
Abstract
Gastrointestinal (GI) cancers remain a leading cause of cancer-related morbidity and mortality worldwide. Artificial intelligence (AI), particularly machine learning and deep learning (DL), has shown promise in enhancing cancer detection, diagnosis, and prognostication. A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed, Web of Science, and Scopus. Search terms included "gastrointestinal cancer", "artificial intelligence", "machine learning", "deep learning", "radiomics", "multimodal detection" and "predictive modeling". Studies were included if they focused on clinically relevant AI applications in GI oncology. AI algorithms for GI cancer detection have achieved high performance across imaging modalities, with endoscopic DL systems reporting accuracies of 85%-97% for polyp detection and segmentation. Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92. Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists (78.9% vs 80.0%), though without incremental value when combined with human interpretation. Multimodal AI approaches integrating imaging, pathology, and clinical data show emerging potential for precision oncology. AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks, with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care. However, broader validation, integration into clinical workflows, and attention to ethical, legal, and social implications remain critical for widespread adoption.
Core Tip: Gastrointestinal (GI) cancers remain a major global health burden, demanding better early detection and personalized treatments. Recent artificial intelligence (AI) advances enable precision oncology by integrating diverse data from endoscopic images to genomic profiles. AI-driven tools enhance polyp detection, tumor grading, and multi-omics analysis for tailored therapies. Despite challenges in standardization and clinical adoption, these innovations promise to reduce diagnostic disparities and improve outcomes in GI cancer care.
