Kotelevets SM. From gastric cancer prevention to global health care, this is the way to implementing artificial intelligence in medicine. Artif Intell Cancer 2026; 7(1): 118079 [DOI: 10.35713/aic.v7.i1.118079]
Corresponding Author of This Article
Sergey M Kotelevets, MD, Professor, Department of Propaedeutics of Internal Medicine, North Caucasus State Academy, Stavropolskaya Street 36, Cherkessk 369000, Karachay-Cherkess Republic, Russia. smkotelevets@mail.ru
Research Domain of This Article
Gastroenterology & Hepatology
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review-article
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Kotelevets SM. From gastric cancer prevention to global health care, this is the way to implementing artificial intelligence in medicine. Artif Intell Cancer 2026; 7(1): 118079 [DOI: 10.35713/aic.v7.i1.118079]
From gastric cancer prevention to global health care, this is the way to implementing artificial intelligence in medicine
Sergey M Kotelevets
Sergey M Kotelevets, Department of Propaedeutics of Internal Medicine, North Caucasus State Academy, Cherkessk 369000, Karachay-Cherkess Republic, Russia
Author contributions: Kotelevets SM contributed to this paper, designed the overall concept and outline of the manuscript, contributed to the design of the manuscript, contributed to the writing and editing the manuscript, illustrations, and review of literature.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Sergey M Kotelevets, MD, Professor, Department of Propaedeutics of Internal Medicine, North Caucasus State Academy, Stavropolskaya Street 36, Cherkessk 369000, Karachay-Cherkess Republic, Russia. smkotelevets@mail.ru
Received: December 23, 2025 Revised: January 15, 2026 Accepted: February 3, 2026 Published online: September 8, 2026 Processing time: 253 Days and 15.8 Hours
Abstract
The use of artificial intelligence (AI) models for gastric cancer prevention shows promise beyond traditional diagnostic approaches. Modern digital technologies can be applied to global healthcare. The implementation of deep machine learning has significantly increased the efficiency of computer vision. The introduction of AI is very effective in the field of diagnostic recognition of pathology in endoscopic, radiological and histological images. Robotic surgery has good development prospects. Also, many methods of treating diseases are implemented using AI technologies. Comparative studies of the effectiveness of a human doctor and AI have a high level of evidence. Comparative studies report high levels of performance for AI vs human clinicians in selected tasks, although evidence varies by task and dataset. AI demonstrates higher efficiency than 5-10 highly qualified experts in many areas of medicine. The main areas of medicine actively use AI: Diagnostic recognition of X-ray computed tomographic and magnetic resonance images, endoscopic and histological patterns. The use of AI has begun in the field of targeted treatment. The development of robot-associated surgery continues. There is a good prospect for using AI not only for recognizing X-ray computer images, in endoscopy, histology and targeted treatment, but also for subjective methods of examining patients. For example, the development of AI models for questioning patients by correspondence or conversation between the interlocutor - a doctor and the interlocutor-a patient has begun. The number of assistants (interlocutors, digital agents) can be more than two. The language of communication can also be any.
Core Tip: Modern digital technologies can be applied in global healthcare. The introduction of deep machine learning has significantly increased the effectiveness of computer vision. Pathology recognition in endoscopic, radiological, and histological images holds promise in the diagnosis of internal diseases. Physical diagnostic methods (visual examination, palpation, percussion, and auscultation) using artificial intelligence (AI) have demonstrated high diagnostic effectiveness. Comparative studies of the effectiveness of human physicians and AI have a high level of evidence. Development of AI models for interviewing patients via correspondence or in a conversational format between a physician and a patient has begun.
