Artificial intelligence technologies in diagnosis and monitoring of autoimmune gastritis
DOI: 10.32364/2587-6821-2026-10-5-7
Artificial intelligence technologies in diagnosis and monitoring of autoimmune gastritis
M.A. Livzan1, S.I. Mozgovoy1, O.V. Gaus1, A.V. Gubanova2, A.E. Samotuga3, S.S. Zhumazhanova3
1 Omsk State Medical University, Omsk, Russian Federation
2 City Outpatient Hospital No. 8, Omsk, Russian Federation
3 Omsk State Technical University, Omsk, Russian Federation
The current stage of development of artificial intelligence (AI) technologies in medicine is characterized by a transition from a general demonstration of AI technical capabilities to development of clinically significant systems focused on solving specific diagnostic tasks and supporting a practitioner in real clinical practice. As for gastroenterology, this is especially evident in endoscopic and morphological data analysis where AI methods have already demonstrated high efficacy in detection of early and pronounced precancerous changes in the gastric mucosa (GM). However, a number of specialized solutions for autoimmune gastritis (AIG) is still limited. The issue is urgent because early accurate diagnosis of autoimmune GM lesions is still complicated in real-world practice due to high prevalence of Helicobacter pylori and frequent overlap of clinical and morphological signs of various forms of chronic gastritis. At the same time, most modern AI solutions reported in the literature are focused either on analysis of endoscopic images or on evaluation of morphological specimens. There is limited number of the solutions dedicated to integration of various signs within a single explicable clinical diagnostic model suitable for routine use. The paper demonstrates an analysis of modern AI technology capabilities in AIG-focused diagnosis of chronic gastritis, as well as presentation of the authors' own experience in algorithmization of diagnostic search based on clinical, laboratory, endoscopic and morphological data in patients with various H. pylori infection status.
Keywords: artificial intelligence, chronic gastritis, autoimmune gastritis, Helicobacter pylori, chronic atrophic gastritis, digital pathology, medical decision support system.
For citation: Livzan M.A., Mozgovoy S.I., Gaus O.V., Gubanova A.V., Samotuga A.E., Zhumazhanova S.S. Artificial intelligence technologies in diagnosis and monitoring of autoimmune gastritis. Russian Medical Inquiry. 2026;10(5):309–318 (in Russ.). DOI: 10.32364/2587-6821-2026-10-5-7
About the authors:
Maria A. Livzan — Corresponding Member of the RAS, Dr. Sc. (Med.), Professor, Head of the Department of Faculty Therapy and Gastroenterology, Rector of the Omsk State Medical University; 12, Lenin str., Omsk, 644099, Russian Federation; ORCID iD 0000-0001-6581-7017
Sergey I. Mozgovoy — Dr. Sc. (Med.), Assistant Professor, Professor of the Department of Pathological Anatomy, Omsk State University; 12, Lenin str., Omsk, 644099, Russian Federation; ORCID iD 0000-0001-7200-7082
Olga V. Gaus — Dr. Sc. (Med.), Assistant Professor, Professor of the Department of Faculty Therapy and Gastroenterology, Omsk State Medical University; 12, Lenin str., Omsk, 644099, Russian Federation; ORCID iD 0000-0001-9370-4768
Anastasia V. Gubanova — C. Sc. (Med.), Gastroenterologist, City Outpatient Hospital No. 8; 10, Bagration str., Omsk, 644052, Russian Federation; ORCID iD 0000-0002-5989-6804
Alexander E. Samotuga — C. Sc. (Tech.), Assistant Professor of the Department of Integrated Information Security, Omsk State Technical University; 11, Mira ave., Omsk, 644050, Russian Federation; ORCID iD 0000-0001-5647-7498
Samal S. Zhumazhanova — C. Sc. (Tech.), Assistant Professor of the Department of Integrated Information Security, Omsk State Technical University; 11, Mira ave., Omsk, 644050, Russian Federation; ORCID iD 0000-0002-6785-5201
Contact information: Olga V. Gaus, e-mail: gaus_olga@bk.ru
Financial Disclosure: the paper was supported by RSF (project No. 26-15-20051).
There is no conflict of interest.
Received 10.04.2026.
Revised 27.04.2026.
Accepted 19.05.2026.
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