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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">vtmed</journal-id><journal-title-group><journal-title xml:lang="ru">Виртуальные технологии в медицине</journal-title><trans-title-group xml:lang="en"><trans-title>Virtual Technologies in Medicine</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2686-7958</issn><issn pub-type="epub">2687-0037</issn><publisher><publisher-name>РОСОМЕД</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.46594/2687-0037_2026_3_2219</article-id><article-id custom-type="elpub" pub-id-type="custom">vtmed-2219</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ТЕЗИСЫ</subject></subj-group></article-categories><title-group><article-title>Единый медицинский интеллект России (ЕМИР): ИИ-платформа для генерации клинических кейсов и тренинга диагностического мышления студентов-медиков</article-title><trans-title-group xml:lang="en"><trans-title>Unified Medical Intelligence of Russia: An AI platform for generating clinical cases and training diagnostic thinking of medical students</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Борисов</surname><given-names>К. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Borisov</surname><given-names>К. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>ИИ</p></bio><email xlink:type="simple">KB-2020@MAIL.RU</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff xml:lang="ru" id="aff-1"><institution>Первый МГМУ им. И. М. Сеченова</institution></aff><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>28</day><month>09</month><year>2026</year></pub-date><volume>0</volume><issue>3</issue><fpage>145</fpage><lpage>146</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Борисов К.В., 2026</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="ru">Борисов К.В.</copyright-holder><copyright-holder xml:lang="en">Borisov К.V.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.medsim.ru/jour/article/view/2219">https://www.medsim.ru/jour/article/view/2219</self-uri><abstract><p>Представлен образовательный модуль платформы «ЕМИР» — ИИ-симулятор клинического мышления студентов-медиков. В отличие от статических симуляторов, система генерирует уникальные клинические кейсы на основе 64 клинических рекомендаций Минздрава через RAG + YandexGPT. Реализованы 4 уровня сложности, включая коморбидность (сочетание 2–3 заболеваний), и диалоговый режим «Живой приём» для сбора анамнеза. Автоматическая проверка ответов с разбором ошибок. Точность диагноза — 94%, время ответа — 3–5 секунд. Получены свидетельства Роспатента, проект поддержан Сеченовским Университетом и МЧС России.</p></abstract><trans-abstract xml:lang="en"><p>The educational module of the Unified Medical Intelligence of Russia platform, an AI simulator of clinical thinking of medical students, is presented. Unlike static simulators, the system generates unique clinical cases based on 64 clinical recommendations from the Ministry of Health through RAG + YandexGPT. 4 levels of difficulty have been implemented, including comorbidity (a combination of 2-3 diseases), and a Live Reception dialog mode for collecting medical history. Automatic verification of responses with error analysis. The accuracy of the diagnosis is 94%, the response time is 3-5 seconds. Rospatent certificates have been received, and the project is supported by Sechenov University and the Russian Ministry of Emergency Situations.</p></trans-abstract></article-meta></front><back><ref-list><title>References</title></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
