Artificial intelligence technologies in diagnosis and monitoring of autoimmune gastritis

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