<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<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">russjcardiol</journal-id><journal-title-group><journal-title xml:lang="ru">Российский кардиологический журнал</journal-title><trans-title-group xml:lang="en"><trans-title>Russian Journal of Cardiology</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1560-4071</issn><issn pub-type="epub">2618-7620</issn><publisher><publisher-name>«SILICEA-POLIGRAF» LLC</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.15829/1560-4071-2026-6878</article-id><article-id custom-type="edn" pub-id-type="custom">UZGHCW</article-id><article-id custom-type="elpub" pub-id-type="custom">russjcardiol-6878</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>ОБЗОРЫ И ТЕХНОЛОГИИ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В КАРДИОЛОГИИ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>REVIEWS AND TECHNOLOGIES OF ARTIFICIAL INTELLIGENCE IN CARDIOLOGY</subject></subj-group></article-categories><title-group><article-title>Перспективы применения искусственного интеллекта в кардиохирургии: систематический обзор</article-title><trans-title-group xml:lang="en"><trans-title>Potential of artificial intelligence in cardiac surgery: a systematic review</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0003-3654-1618</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Шацкий</surname><given-names>А. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Shatskiy</surname><given-names>A. S.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Александр Сергеевич Шацкий — к.м.н., докторант-соискатель Института коронарной и сосудистой хирургии; зам. директора по научной работе, Master of Science (Болонский университет)</p><p>Ленинский проспект, д. 8, корп. 7, Москва, 117931; Спортивная ул., д. 114, Иннополис, Республика Татарстан, 420500</p><p> </p></bio><bio xml:lang="en"><p>Leninsky Prospekt, 8, bld. 7, Moscow, 117931; Sportivnaya str., 114, Innopolis, Republic of Tatarstan, 420500</p></bio><email xlink:type="simple">alexander.shatsky@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0005-9668-1211</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Масютина</surname><given-names>С. Е.</given-names></name><name name-style="western" xml:lang="en"><surname>Masyutina</surname><given-names>S. E.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Стефания Евгеньевна Масютина — в.н.с.; н.с., Master of Science (Политехнический университет Милана)</p><p>Спортивная ул., д. 114, Иннополис, Республика Татарстан, 420500; Piazza Leonardo da Vinci, 32, Milan, Italy</p></bio><bio xml:lang="en"><p>Sportivnaya str., 114, Innopolis, Republic of Tatarstan, 420500; Piazza Leonardo da Vinci, 32, Milan, Italy.</p></bio><email xlink:type="simple">skaydrite1806@gmail.com</email><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7444-9930</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Мамалыга</surname><given-names>М. Л.</given-names></name><name name-style="western" xml:lang="en"><surname>Mamalyga</surname><given-names>M. L.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Максим Леонидович Мамалыга — д.м.н., в.н.с. Института коронарной и сосудистой хирургии </p><p>Ленинский проспект, д. 8, корп. 7, Москва, 117931</p></bio><bio xml:lang="en"><p>Leninsky Prospekt, 8, bld. 7, Moscow, 117931</p></bio><email xlink:type="simple">mamalyga83@mail.ru</email><xref ref-type="aff" rid="aff-3"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>ФГБУ Научный медицинский исследовательский центр сердечно-сосудистой хирургии им. А. Н. Бакулева Минздрава России; &#13;
МИП "Лаборатория передовых технологий" при Университете Иннополис</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Bakulev National Medical Research Center for Cardiovascular Surgery; &#13;
Innopolis University Advanced Technologies Laboratory</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>МИП "Лаборатория передовых технологий" при Университете Иннополис; &#13;
Миланский политехнический университет</institution><country>Италия</country></aff><aff xml:lang="en"><institution>Innopolis University Advanced Technologies Laboratory; &#13;
Polytechnic University of Milan</institution><country>Italy</country></aff></aff-alternatives><aff-alternatives id="aff-3"><aff xml:lang="ru"><institution>ФГБУ Научный медицинский исследовательский центр сердечно-сосудистой хирургии им. А. Н. Бакулева Минздрава России</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Bakulev National Medical Research Center for Cardiovascular Surgery</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>22</day><month>04</month><year>2026</year></pub-date><volume>31</volume><issue>2S</issue><issue-title>Искусственный интеллект в медицине</issue-title><fpage>6878</fpage><lpage>6878</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Шацкий А.С., Масютина С.Е., Мамалыга М.Л., 2026</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="ru">Шацкий А.С., Масютина С.Е., Мамалыга М.Л.</copyright-holder><copyright-holder xml:lang="en">Shatskiy A.S., Masyutina S.E., Mamalyga M.L.</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://russjcardiol.elpub.ru/jour/article/view/6878">https://russjcardiol.elpub.ru/jour/article/view/6878</self-uri><abstract><p>Внедрение технологий искусственного интеллекта (ИИ) открывает новые горизонты в кардиохирургии, однако для безопасной клинической практики требуется систематизация данных о его возможностях и ограничениях. Целью работы является систематизация современных данных о применении ИИ в кардиохирургии и определение перспективных направлений его клинического внедрения. В данном систематическом обзоре, выполненном по базам PubMed, Scopus, Cochrane Library, Google Scholar и Web of Science за период 2000-2025гг в соответствии с критериями PRISMA, проанализированы исследования, посвященные применению ИИ на всех этапах кардиохирургического лечения. Согласно результатам анализа 179 исследований, модели машинного обучения демонстрируют более высокую чувствительность по сравнению с традиционными методами диагностики и шкалами риска при прогнозировании послеоперационных исходов и осложнений. Роботизированные системы на основе ИИ и компьютерное зрение способны повысить точность оперативных вмешательств, а использование ИИ для послеоперационного мониторинга способствует улучшению исходов и результатов реабилитации пациентов. Основными барьерами для масштабирования технологий остаются недостаточность данных, этические аспекты и сложность интеграции в клинические процессы. Таким образом, ИИ способен улучшить качество кардиохирургической помощи, однако для реализации этого потенциала необходимы валидация алгоритмов, устранение системных ошибок и разработка прозрачных этических и правовых норм.</p></abstract><trans-abstract xml:lang="en"><p>The introduction of artificial intelligence (AI) technologies pushes the boundaries in cardiac surgery, but safe clinical practice requires systematization of data on its capabilities and limitations. The study aim is to systematize current data on the use of AI in cardiac surgery and identify promising areas for its clinical implementation. This systematic review of data from PubMed, Scopus, the Cochrane Library, Google Scholar, and Web of Science for the period 2000-2025 in accordance with the PRISMA criteria analyzed studies on AI use at all stages of cardiac surgery. Based on the analysis of 179 studies, machine learning models demonstrate higher sensitivity compared to traditional diagnostic methods and risk scores in predicting postoperative outcomes and complications. AI-based robotic systems and computer vision can improve the precision of surgical interventions, and the use of AI for postoperative monitoring can improve patient outcomes and rehabilitation. The main barriers to scaling these technologies remain data insufficiency, ethical considerations, and the difficulty of integrating them into clinical practice. Thus, AI has the potential to improve the quality of cardiac surgical care. However, realizing this potential requires validating algorithms, eliminating systemic errors, and developing transparent ethical and legal standards.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>искусственный интеллект</kwd><kwd>кардиохирургия</kwd><kwd>систематический обзор</kwd><kwd>большие языковые модели</kwd></kwd-group><kwd-group xml:lang="en"><kwd>artificial intelligence</kwd><kwd>cardiac surgery</kwd><kwd>systematic review</kwd><kwd>large-scale language models</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Cuocolo R, Perillo T, De Rosa E, et al. Current applications of big data and machine learning in cardiology. J Geriatr Cardiol. 2019;16(8):601-7. doi:10.11909/j.issn.16715411.2019.08.002.</mixed-citation><mixed-citation xml:lang="en">Cuocolo R, Perillo T, De Rosa E, et al. Current applications of big data and machine learning in cardiology. J Geriatr Cardiol. 2019;16(8):601-7. doi:10.11909/j.issn.16715411.2019.08.002.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Seetharam K, Balla S, Bianco C, et al. Applications of machine learning in cardiology. Cardiology and Therapy. 2022;11(3):355-68. doi:10.1007/s40119-022-00273-7.</mixed-citation><mixed-citation xml:lang="en">Seetharam K, Balla S, Bianco C, et al. Applications of machine learning in cardiology. Cardiology and Therapy. 2022;11(3):355-68. doi:10.1007/s40119-022-00273-7.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Fjelland R. Why General Artificial Intelligence will not be realized. Humanities and Social Sciences Communications. 2020;7(1). doi:10.1057/s41599-020-0494-4.</mixed-citation><mixed-citation xml:lang="en">Fjelland R. Why General Artificial Intelligence will not be realized. Humanities and Social Sciences Communications. 2020;7(1). doi:10.1057/s41599-020-0494-4.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Sarraju A, Ouyang D, Itchhaporia D. The Opportunities and Challenges of Large Language Models in Cardiology. JACC: Advances. 2023;2(7):100438. doi:10.1016/j.jacadv. 2023.100438.</mixed-citation><mixed-citation xml:lang="en">Sarraju A, Ouyang D, Itchhaporia D. The Opportunities and Challenges of Large Language Models in Cardiology. JACC: Advances. 2023;2(7):100438. doi:10.1016/j.jacadv. 2023.100438.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med. 2023;388:1233-9. doi:10.1056/NEJMsr2214184.</mixed-citation><mixed-citation xml:lang="en">Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med. 2023;388:1233-9. doi:10.1056/NEJMsr2214184.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Wu E, Wu K, Daneshjou R, et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med. 2021;27:582-4. doi:10.1038/s41591-021-01312-x.</mixed-citation><mixed-citation xml:lang="en">Wu E, Wu K, Daneshjou R, et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med. 2021;27:582-4. doi:10.1038/s41591-021-01312-x.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Skalidis I, Cagnina A, Fournier S. Use of large language models for evidence-based cardiovascular medicine. Eur Heart J Digit Health. 2023;4(5):368-9. doi:10.1093/ehjdh/ztad041.</mixed-citation><mixed-citation xml:lang="en">Skalidis I, Cagnina A, Fournier S. Use of large language models for evidence-based cardiovascular medicine. Eur Heart J Digit Health. 2023;4(5):368-9. doi:10.1093/ehjdh/ztad041.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Klang E, Cohen-Shelly M, Lopez-Jimenez F. Leveraging Large Language Models to Enhance Digital Health in Cardiology: A Preview of a Cutting-Edge Language Generation Model. Mayo Clin Proc: Digital Health. 2023;1(2):105-8. doi:10.1016/j.mcpdig.2023.03.003.</mixed-citation><mixed-citation xml:lang="en">Klang E, Cohen-Shelly M, Lopez-Jimenez F. Leveraging Large Language Models to Enhance Digital Health in Cardiology: A Preview of a Cutting-Edge Language Generation Model. Mayo Clin Proc: Digital Health. 2023;1(2):105-8. doi:10.1016/j.mcpdig.2023.03.003.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Smith M, Sattler A, Hong G, Lin S. From code to bedside: implementing artificial intelligence using quality improvement methods. Journal of General Internal Medicine. 2021;36(4):10616. doi:10.1007/s11606-020-06394-w.</mixed-citation><mixed-citation xml:lang="en">Smith M, Sattler A, Hong G, Lin S. From code to bedside: implementing artificial intelligence using quality improvement methods. Journal of General Internal Medicine. 2021;36(4):10616. doi:10.1007/s11606-020-06394-w.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Petersson L, Larsson I, Nygren JM, et al. Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research. 2022;22(1):850. doi:10.1186/s12913-022-08215-8.</mixed-citation><mixed-citation xml:lang="en">Petersson L, Larsson I, Nygren JM, et al. Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research. 2022;22(1):850. doi:10.1186/s12913-022-08215-8.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">van Stekelenborg J, Ellenius J, Maskell S, et al. Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR. Drug Safety. 2019; 42(12):1393407. doi:10.1007/s40264-019-00858-7.</mixed-citation><mixed-citation xml:lang="en">van Stekelenborg J, Ellenius J, Maskell S, et al. Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR. Drug Safety. 2019; 42(12):1393407. doi:10.1007/s40264-019-00858-7.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Basile AO, Yahi A, Tatonetti NP. Artificial Intelligence for Drug Toxicity and Safety. Trends in Pharmacological Sciences. 2019;40(9):624-35. doi:10.1016/j.tips.2019.07.005.</mixed-citation><mixed-citation xml:lang="en">Basile AO, Yahi A, Tatonetti NP. Artificial Intelligence for Drug Toxicity and Safety. Trends in Pharmacological Sciences. 2019;40(9):624-35. doi:10.1016/j.tips.2019.07.005.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Barreda M, Cantarero-Prieto D, Coca D, et al. Transforming healthcare with chatbots: Uses and applications-A scoping review. Digit Health. 2025;11. doi:10.1177/20552076251319174.</mixed-citation><mixed-citation xml:lang="en">Barreda M, Cantarero-Prieto D, Coca D, et al. Transforming healthcare with chatbots: Uses and applications-A scoping review. Digit Health. 2025;11. doi:10.1177/20552076251319174.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018;71(23):2668-79. doi:10.1016/j.jacc.2018.03.521.</mixed-citation><mixed-citation xml:lang="en">Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018;71(23):2668-79. doi:10.1016/j.jacc.2018.03.521.</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Murcia VM, Aggarwal V, Pesaldinne N, et al. Automating clinical trial matches via natural language processing of synthetic electronic health records and clinical trial eligibility criteria. AMIA Jt Summits Transl Sci Proc. 2024;2024:125-34.</mixed-citation><mixed-citation xml:lang="en">Murcia VM, Aggarwal V, Pesaldinne N, et al. Automating clinical trial matches via natural language processing of synthetic electronic health records and clinical trial eligibility criteria. AMIA Jt Summits Transl Sci Proc. 2024;2024:125-34.</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Bowdish ME, D’Agostino RS, Thourani VH, et al. STS Adult Cardiac Surgery Database: 2021 Update on Outcomes, Quality, and Research. Ann Thorac Surg. 2021;111(6):177080. doi:10.1016/j.athoracsur.2021.03.043.</mixed-citation><mixed-citation xml:lang="en">Bowdish ME, D’Agostino RS, Thourani VH, et al. STS Adult Cardiac Surgery Database: 2021 Update on Outcomes, Quality, and Research. Ann Thorac Surg. 2021;111(6):177080. doi:10.1016/j.athoracsur.2021.03.043.</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021;18(7):46578. doi:10.1038/s41569-020-00503-2.</mixed-citation><mixed-citation xml:lang="en">Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021;18(7):46578. doi:10.1038/s41569-020-00503-2.</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Yao X, Rushlow DR, Inselman JW, et al. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med. 2021;27(5):815-9. doi:10.1038/s41591-021-01335-4.</mixed-citation><mixed-citation xml:lang="en">Yao X, Rushlow DR, Inselman JW, et al. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med. 2021;27(5):815-9. doi:10.1038/s41591-021-01335-4.</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Mamalakis M, Garg P, Nelson T, et al. Artificial Intelligence framework with traditional computer vision and deep learning approaches for optimal automatic segmentation of left ventricle with scar. Artificial Intelligence in Medicine. 2023:102610. doi:10.1016/j.artmed.2023.102610.</mixed-citation><mixed-citation xml:lang="en">Mamalakis M, Garg P, Nelson T, et al. Artificial Intelligence framework with traditional computer vision and deep learning approaches for optimal automatic segmentation of left ventricle with scar. Artificial Intelligence in Medicine. 2023:102610. doi:10.1016/j.artmed.2023.102610.</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020;580(7802):252-6. doi:10.1038/s41586-020-2145-8.</mixed-citation><mixed-citation xml:lang="en">Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020;580(7802):252-6. doi:10.1038/s41586-020-2145-8.</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Bustin A, Fuin N, Botnar RM, Prieto C. From Compressed-Sensing to Artificial Intelligence Based Cardiac MRI Reconstruction. Front Cardiovasc Med. 2020;(7):17. doi:10.3389/fcvm.2020.00017.</mixed-citation><mixed-citation xml:lang="en">Bustin A, Fuin N, Botnar RM, Prieto C. From Compressed-Sensing to Artificial Intelligence Based Cardiac MRI Reconstruction. Front Cardiovasc Med. 2020;(7):17. doi:10.3389/fcvm.2020.00017.</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Monti CB, Codari M, van Assen M, et al. Machine Learning and Deep Neural Networks Applications in Computed Tomography for Coronary Artery Disease and Myocardial Perfusion. J Thorac Imaging. 2020;35 Suppl 1: S58-S65. doi:10.1097/rti.0000000000000490.</mixed-citation><mixed-citation xml:lang="en">Monti CB, Codari M, van Assen M, et al. Machine Learning and Deep Neural Networks Applications in Computed Tomography for Coronary Artery Disease and Myocardial Perfusion. J Thorac Imaging. 2020;35 Suppl 1: S58-S65. doi:10.1097/rti.0000000000000490.</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Cau R, Flanders A, Mannelli L, et al. Artificial intelligence in computed tomography plaque characterization: A review. Eur J Radiol. 2021;140:109767. doi:10.1016/j.ejrad.2021.109767.</mixed-citation><mixed-citation xml:lang="en">Cau R, Flanders A, Mannelli L, et al. Artificial intelligence in computed tomography plaque characterization: A review. Eur J Radiol. 2021;140:109767. doi:10.1016/j.ejrad.2021.109767.</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Muscogiuri G, Chiesa M, Baggiano A, et al. Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion. Eur J Nucl Med Mol Imaging. 2022;49(9):3119-28. doi:10.1007/s00259-022-05732-w.</mixed-citation><mixed-citation xml:lang="en">Muscogiuri G, Chiesa M, Baggiano A, et al. Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion. Eur J Nucl Med Mol Imaging. 2022;49(9):3119-28. doi:10.1007/s00259-022-05732-w.</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">Seo JW, Park S, Kim YJ, et al. Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach. Sci Rep. 2023;13:967. doi:10.1038/s41598022-25849-0.</mixed-citation><mixed-citation xml:lang="en">Seo JW, Park S, Kim YJ, et al. Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach. Sci Rep. 2023;13:967. doi:10.1038/s41598022-25849-0.</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Popescu C, Laudicella R, Baldari S, et al. PET-based artificial intelligence applications in cardiac nuclear medicine. Swiss Med Wkly. 2022;152:w30123. doi:10.4414/smw.2022.w30123.</mixed-citation><mixed-citation xml:lang="en">Popescu C, Laudicella R, Baldari S, et al. PET-based artificial intelligence applications in cardiac nuclear medicine. Swiss Med Wkly. 2022;152:w30123. doi:10.4414/smw.2022.w30123.</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Dundas J, Leipsic JA, Sellers S, et al. Artificial intelligence-based coronary stenosis quantification at coronary CT angiography versus quantitative coronary angiography. Radiol Cardiothorac Imaging. 2023;5(6):e230124. doi:10.1148/ryct.230124.</mixed-citation><mixed-citation xml:lang="en">Dundas J, Leipsic JA, Sellers S, et al. Artificial intelligence-based coronary stenosis quantification at coronary CT angiography versus quantitative coronary angiography. Radiol Cardiothorac Imaging. 2023;5(6):e230124. doi:10.1148/ryct.230124.</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Kruzhilov I, Mazanov G, Ponomarchuk A, et al. Coronary Dominance: Angiogram dataset for coronary dominance classification. Sci Data. 2025;12(1):341. doi:10.1038/s41597025-04676-8.</mixed-citation><mixed-citation xml:lang="en">Kruzhilov I, Mazanov G, Ponomarchuk A, et al. Coronary Dominance: Angiogram dataset for coronary dominance classification. Sci Data. 2025;12(1):341. doi:10.1038/s41597025-04676-8.</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Kruzhilov I, Ikryannikov E, Shadrin A, et al. Neural Network-Based Coronary Dominance Classification of RCA Angiograms. Dokl Math. 2024;110(Suppl 1): S212-S222. doi:10.1134/S1064562424602026.</mixed-citation><mixed-citation xml:lang="en">Kruzhilov I, Ikryannikov E, Shadrin A, et al. Neural Network-Based Coronary Dominance Classification of RCA Angiograms. Dokl Math. 2024;110(Suppl 1): S212-S222. doi:10.1134/S1064562424602026.</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Saka E, Öztürk E, Yüksel AE, Kocabaş NS. Comparison of EuroSCORE II and STS Risk Scoring Systems in Patients who Underwent Open-heart Surgery. Turk J Anaesthesiol Reanim. 2025;53(4):163-9. doi:10.4274/TJAR.2025.241778.</mixed-citation><mixed-citation xml:lang="en">Saka E, Öztürk E, Yüksel AE, Kocabaş NS. Comparison of EuroSCORE II and STS Risk Scoring Systems in Patients who Underwent Open-heart Surgery. Turk J Anaesthesiol Reanim. 2025;53(4):163-9. doi:10.4274/TJAR.2025.241778.</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Maier-Hein L, Eisenmann M, Sarikaya D, et al. Surgical data science — from concepts toward clinical translation. Med Image Anal. 2022;76:102306. doi:10.1016/j.media.2021.102306.</mixed-citation><mixed-citation xml:lang="en">Maier-Hein L, Eisenmann M, Sarikaya D, et al. Surgical data science — from concepts toward clinical translation. Med Image Anal. 2022;76:102306. doi:10.1016/j.media.2021.102306.</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">Liu Z, Zhang C, Ge S. Efficacy and safety of robotic-assisted versus median sternotomy for cardiac surgery: results from a university affiliated hospital. J Thorac Dis. 2023;15(4):1861-71. doi:10.21037/jtd-23-197.</mixed-citation><mixed-citation xml:lang="en">Liu Z, Zhang C, Ge S. Efficacy and safety of robotic-assisted versus median sternotomy for cardiac surgery: results from a university affiliated hospital. J Thorac Dis. 2023;15(4):1861-71. doi:10.21037/jtd-23-197.</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">Nedadur R, Bhatt N, Chung J, et al. Canadian Thoracic Aortic Collaborative. Machine learning and decision making in aortic arch repair. J Thorac Cardiovasc Surg. 2025;169(1):5967.e4. doi:10.1016/j.jtcvs.2023.11.032.</mixed-citation><mixed-citation xml:lang="en">Nedadur R, Bhatt N, Chung J, et al. Canadian Thoracic Aortic Collaborative. Machine learning and decision making in aortic arch repair. J Thorac Cardiovasc Surg. 2025;169(1):5967.e4. doi:10.1016/j.jtcvs.2023.11.032.</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">Li B., Peng P, Yue G, et al. Efficacy and Safety of a Novel Multi-Channel Vascular Interventional Robotic System: Animal Studies. CVIA. 2025;10(1). doi:10.15212/CVIA.2025.0018.</mixed-citation><mixed-citation xml:lang="en">Li B., Peng P, Yue G, et al. Efficacy and Safety of a Novel Multi-Channel Vascular Interventional Robotic System: Animal Studies. CVIA. 2025;10(1). doi:10.15212/CVIA.2025.0018.</mixed-citation></citation-alternatives></ref><ref id="cit35"><label>35</label><citation-alternatives><mixed-citation xml:lang="ru">Sadeghi AH, Maat APWM, Taverne YJHJ, et al. Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentectomies. JTCVS Tech. 2021;7:309-21. doi:10.1016/j.xjtc.2021.03.016.</mixed-citation><mixed-citation xml:lang="en">Sadeghi AH, Maat APWM, Taverne YJHJ, et al. Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentectomies. JTCVS Tech. 2021;7:309-21. doi:10.1016/j.xjtc.2021.03.016.</mixed-citation></citation-alternatives></ref><ref id="cit36"><label>36</label><citation-alternatives><mixed-citation xml:lang="ru">Lo Muzio FP, Rozzi G, Rossi S, et al. Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects. J Clin Med. 2021;10(22):5330. doi:10.3390/jcm10225330.</mixed-citation><mixed-citation xml:lang="en">Lo Muzio FP, Rozzi G, Rossi S, et al. Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects. J Clin Med. 2021;10(22):5330. doi:10.3390/jcm10225330.</mixed-citation></citation-alternatives></ref><ref id="cit37"><label>37</label><citation-alternatives><mixed-citation xml:lang="ru">Hayıroğlu Mİ, Altay S. The Role of Artificial Intelligence in Coronary Artery Disease and Atrial Fibrillation. Balkan Med J. 2023;40(3):151-2. doi:10.4274/balkanmedj.galenos.2023.06042023.</mixed-citation><mixed-citation xml:lang="en">Hayıroğlu Mİ, Altay S. The Role of Artificial Intelligence in Coronary Artery Disease and Atrial Fibrillation. Balkan Med J. 2023;40(3):151-2. doi:10.4274/balkanmedj.galenos.2023.06042023.</mixed-citation></citation-alternatives></ref><ref id="cit38"><label>38</label><citation-alternatives><mixed-citation xml:lang="ru">Arpaia P, Crauso F, De Benedetto E, et al. Soft Transducer for Patient’s Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection. Sensors (Basel). 2022;22(2):536. doi:10.3390/s22020536.</mixed-citation><mixed-citation xml:lang="en">Arpaia P, Crauso F, De Benedetto E, et al. Soft Transducer for Patient’s Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection. Sensors (Basel). 2022;22(2):536. doi:10.3390/s22020536.</mixed-citation></citation-alternatives></ref><ref id="cit39"><label>39</label><citation-alternatives><mixed-citation xml:lang="ru">Gentry A, Mongan WM, Lee B, et al. Activity Segmentation Using Wearable Sensors for DVT/PE Risk Detection. Proc COMPSAC. 2019;2019:477-83. doi:10.1109/compsac.2019.10252.</mixed-citation><mixed-citation xml:lang="en">Gentry A, Mongan WM, Lee B, et al. Activity Segmentation Using Wearable Sensors for DVT/PE Risk Detection. Proc COMPSAC. 2019;2019:477-83. doi:10.1109/compsac.2019.10252.</mixed-citation></citation-alternatives></ref><ref id="cit40"><label>40</label><citation-alternatives><mixed-citation xml:lang="ru">Nedadur R, Bhatt N, Liu T, et al. The Emerging and Important Role of Artificial Intelligence in Cardiac Surgery. Can J Cardiol. 2024;40(10):1865-79. doi:10.1016/j.cjca.2024.07.027.</mixed-citation><mixed-citation xml:lang="en">Nedadur R, Bhatt N, Liu T, et al. The Emerging and Important Role of Artificial Intelligence in Cardiac Surgery. Can J Cardiol. 2024;40(10):1865-79. doi:10.1016/j.cjca.2024.07.027.</mixed-citation></citation-alternatives></ref><ref id="cit41"><label>41</label><citation-alternatives><mixed-citation xml:lang="ru">Jeyaraman M, Balaji S, Jeyaraman N, Yadav S. Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare. Cureus. 2023;15(8):e43262. doi:10.7759/cureus.43262.</mixed-citation><mixed-citation xml:lang="en">Jeyaraman M, Balaji S, Jeyaraman N, Yadav S. Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare. Cureus. 2023;15(8):e43262. doi:10.7759/cureus.43262.</mixed-citation></citation-alternatives></ref><ref id="cit42"><label>42</label><citation-alternatives><mixed-citation xml:lang="ru">Rasheed K, Qayyum A, Ghaly M, et al. Explainable, trustworthy, and ethical machine learning for healthcare: A survey. Comput Biol Med. 2022;149:106043. doi:10.1016/j.compbiomed.2022.106043.</mixed-citation><mixed-citation xml:lang="en">Rasheed K, Qayyum A, Ghaly M, et al. Explainable, trustworthy, and ethical machine learning for healthcare: A survey. Comput Biol Med. 2022;149:106043. doi:10.1016/j.compbiomed.2022.106043.</mixed-citation></citation-alternatives></ref><ref id="cit43"><label>43</label><citation-alternatives><mixed-citation xml:lang="ru">Ferrarese A, Pozzi G, Borghi F, et al. Malfunctions of robotic system in surgery: role and responsibility of surgeon in legal point of view. Open Med (Wars). 2016;11(1):28691. doi:10.1515/med-2016-0055.</mixed-citation><mixed-citation xml:lang="en">Ferrarese A, Pozzi G, Borghi F, et al. Malfunctions of robotic system in surgery: role and responsibility of surgeon in legal point of view. Open Med (Wars). 2016;11(1):28691. doi:10.1515/med-2016-0055.</mixed-citation></citation-alternatives></ref><ref id="cit44"><label>44</label><citation-alternatives><mixed-citation xml:lang="ru">Ahsani-Estahbanati E, Gordeev V, Doshmangir L. Interventions to reduce the incidence of medical error and its financial burden in health care systems: A systematic review of systematic reviews. Front Med (Lausanne). 2022;9:875426. doi:10.3389/fmed.2022.875426.</mixed-citation><mixed-citation xml:lang="en">Ahsani-Estahbanati E, Gordeev V, Doshmangir L. Interventions to reduce the incidence of medical error and its financial burden in health care systems: A systematic review of systematic reviews. Front Med (Lausanne). 2022;9:875426. doi:10.3389/fmed.2022.875426.</mixed-citation></citation-alternatives></ref><ref id="cit45"><label>45</label><citation-alternatives><mixed-citation xml:lang="ru">Al Dihan FA, Alghamdi MA, Aldihan FA, et al. Knowledge, Attitude, Awareness, and Future Expectations of Robotic Surgery in Patients Attending Surgical Specialties Clinics. Cureus. 2024;16(3):e56523. doi:10.7759/cureus.56523.</mixed-citation><mixed-citation xml:lang="en">Al Dihan FA, Alghamdi MA, Aldihan FA, et al. Knowledge, Attitude, Awareness, and Future Expectations of Robotic Surgery in Patients Attending Surgical Specialties Clinics. Cureus. 2024;16(3):e56523. doi:10.7759/cureus.56523.</mixed-citation></citation-alternatives></ref></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>
