Kaushik K, Pathania J, Singh PK, Dinkar M, Pathania V. Artificial intelligence and machine learning applications in abdominal tuberculosis diagnosis: A scoping review and translational roadmap. Artif Intell Gastroenterol 2026; 7(2): 123751 [DOI: 10.35712/aig.123751]
Corresponding Author of This Article
Jyoti Pathania, MD, Full Professor, Head, Department of Anesthesia, Rohilkhand Medical College and Hospital, Pilbhit Bypass Road, Bareilly 243006, Uttar Pradesh, India. pathaniajyoti7@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
research-article
Open-Access Policy of This Article
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/
Kumar Kaushik, Department of General Surgery, Rohilkhand Medical College and Hospital, Bareilly 243006, Uttar Pradesh, India
Jyoti Pathania, Department of Anesthesia, Rohilkhand Medical College and Hospital, Bareilly 243006, Uttar Pradesh, India
Pritish Kumar Singh, Department of Gastroenterology, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi 110085, Delhi, India
Monika Dinkar, Department of Obstretrics and Gynaecology, Bhagwan Mahavir Hospital, New Delhi 110034, Delhi, India
Vanya Pathania, Department of Gynaecology and Obstretics, Maharishi Markandeshwar Medical College and Hospital, Solan 173229, Himachal Pradesh, India
Co-first authors: Kumar Kaushik and Jyoti Pathania.
Author contributions: Kaushik K performed the literature search, screened the records, extracted data, conducted the risk of bias assessment, and drafted the initial manuscript; Kaushik K and Pathania J contributed to the study conception and design, and they contributed equally to this manuscript and are co-first authors; Pathania J provided overall supervision, guided the methodology, and critically revised the manuscript for important intellectual content; Singh PK contributed to the clinical interpretation of data related to gastrointestinal tuberculosis and provided critical revisions; Dinkar M assisted with data extraction and performed the statistical analysis; Pathania V assisted in data interpretation and verification. All authors read and approved the final version of the manuscript.
AI contribution statement: AI tool usage is disclosed as follows: Grok by xAI (accessed April 2026) was used for preliminary literature record identification and screening prioritization during the search phase. The initial draft schematic diagrams were generated by Google AI Gemini. Subsequently, the figures for the article were redrawn, so the current images are all hand-drawn by the authors. Grammarly (version accessed April 2026) was employed for language editing, grammar correction, spelling, and punctuation. AI involvement in manuscript preparation was strictly limited to these assistive functions. All screening decisions, data extraction, risk of bias assessment, thematic synthesis, critical interpretation, manuscript drafting, and revision were performed independently by the authors. No AI-generated text was incorporated verbatim into the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: Jyoti Pathania, MD, Full Professor, Head, Department of Anesthesia, Rohilkhand Medical College and Hospital, Pilbhit Bypass Road, Bareilly 243006, Uttar Pradesh, India. pathaniajyoti7@gmail.com
Received: May 28, 2026 Revised: June 12, 2026 Accepted: June 30, 2026 Published online: August 8, 2026 Processing time: 71 Days and 1.6 Hours
Abstract
BACKGROUND
Abdominal tuberculosis (ATB) remains a major diagnostic challenge in high-burden low-income and middle-income countries due to nonspecific symptoms, paucibacillary disease, low microbiological confirmation rates, and mimicry of Crohn’s disease and other conditions. Artificial intelligence (AI) and machine learning have shown promise in pulmonary tuberculosis but their role in ATB diagnosis is still emerging.
AIM
To map the existing evidence on AI applications for ATB diagnosis, and propose a translational roadmap for equitable clinical integration.
METHODS
This Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews-compliant scoping review searched PubMed, Cochrane Library, and Google Scholar up to April 2026. Two independent reviewers screened records and extracted data. Risk of bias was assessed using adapted Prediction Model Risk of Bias Assessment Tool principles. Studies were thematically categorized and narratively synthesized.
RESULTS
Seventeen studies (16 original retrospective studies and one systematic review) were included. Fourteen studies focused on differentiating intestinal tuberculosis from Crohn’s disease using radiomics, endoscopic deep learning, histopathology, and multimodal approaches, with area under the curves ranging from 0.80-0.96. Multimodal and explainable models performed best. Major gaps included the absence of management-focused applications, predominance of composite reference standards, limited geographic diversity (87.5% from China), small ATB sample sizes, and minimal attention to human immunodeficiency virus status or drug-resistant tuberculosis.
CONCLUSION
AI and machine learning demonstrate strong potential to improve abdominal tuberculosis differentiation from mimics; however, prospective validation in diverse low-income and middle-income countries populations and integration with existing diagnostic frameworks remain urgently needed for equitable impact.
Core Tip: This is the first dedicated Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews-compliant scoping review of artificial intelligence and machine learning for abdominal tuberculosis diagnosis and differential diagnosis. It demonstrates high accuracy of radiomics, endoscopic convolutional neural networks, and multimodal models in differentiating abdominal tuberculosis from Crohn’s disease and other mimics, yet reveals a complete absence of management-focused artificial intelligence applications, and predominance of composite reference standards. The proposed four-phase roadmap provides a practical, equity-centered pathway from retrospective evidence to real-world deployment, prioritizing microbiological reference standards, bias audits, infrastructure-appropriate tools, and explicit integration with existing hierarchical diagnostic criteria to reduce diagnostic delays and unnecessary interventions in high-burden settings.
