<?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="review-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">pirogovestnik</journal-id><journal-title-group><journal-title xml:lang="ru">Вестник Национального медико-хирургического центра им. Н.И. Пирогова</journal-title><trans-title-group xml:lang="en"><trans-title>Bulletin of Pirogov National Medical &amp; Surgical Center</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2072-8255</issn><issn pub-type="epub">2782-3628</issn><publisher><publisher-name>Национальный медико-хирургический Центр им. Н.И. Пирогова</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.25881/20728255_2026_21_1_127</article-id><article-id custom-type="elpub" pub-id-type="custom">pirogovestnik-546</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 ЛИТЕРАТУРЫ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>REVIEWS</subject></subj-group></article-categories><title-group><article-title>ПРИМЕНЕНИЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ТРАВМАТОЛОГИИ</article-title><trans-title-group xml:lang="en"><trans-title>THE USE OF ARTIFICIAL INTELLIGENCE IN TRAUMATOLOGY: A SYSTEMATIC REVIEW AND RECOMMENDATIONS FOR CLINICAL PRACTICE</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>Савгачев</surname><given-names>В. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Savgachev</surname><given-names>V. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Ярославль</p></bio><bio xml:lang="en"><p>Yaroslavl</p></bio><email xlink:type="simple">hirurg2288@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Шубин</surname><given-names>Л. Б.</given-names></name><name name-style="western" xml:lang="en"><surname>Shubin</surname><given-names>L. B.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Ярославль</p></bio><bio xml:lang="en"><p>Yaroslavl</p></bio><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>ФГБОУ ВО «Ярославский государственный медицинский университет»</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Yaroslavl State Medical University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>21</day><month>03</month><year>2026</year></pub-date><volume>21</volume><issue>1</issue><fpage>127</fpage><lpage>133</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Савгачев В.В., Шубин Л.Б., 2026</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="ru">Савгачев В.В., Шубин Л.Б.</copyright-holder><copyright-holder xml:lang="en">Savgachev V.V., Shubin L.B.</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://submit.pirogov-vestnik.ru/jour/article/view/546">https://submit.pirogov-vestnik.ru/jour/article/view/546</self-uri><abstract><p>Стремительное развитие технологий искусственного интеллекта (ИИ) в сфере здравоохранения привлекло широкое внимание мирового медицинского сообщества. Особенно заметен потенциал применения ИИ в области травматологии, которая представляет собой гонку со временем. Травма является основной причиной смерти среди людей в возрасте до 40 лет во всем мире, а эффективность и качество лечения напрямую связаны с выживаемостью и прогнозом для пациентов. Традиционная модель лечения травм ограничена такими факторами, как неравномерное распределение медицинских ресурсов, различия в профессиональном опыте и запоздалые диагностические решения, а также существует множество проблем, которые необходимо решать безотлагательно. Вмешательство ИИ открыло возможность революционных изменений в травматологии. Целью данного исследования является всесторонний обзор текущего состояния применения ИИ во всех аспектах травматологии, глубокий анализ его клинической ценности и ограничений, а также предложение практических рекомендаций, основанных на новейших данных.</p></abstract><trans-abstract xml:lang="en"><p>The rapid development of artificial intelligence (AI) technologies in the healthcare sector has attracted widespread attention from the global medical community. The potential of AI applications in the field of traumatology, which is a race against time, is particularly noticeable. Trauma is the leading cause of death among people under the age of 40 worldwide, and the effectiveness and quality of treatment are directly related to survival and prognosis for patients. The traditional trauma treatment model is limited by factors such as uneven distribution of medical resources, differences in professional experience, and delayed diagnostic solutions, and there are many challenges that need to be addressed urgently. The intervention of artificial intelligence has opened up the possibility of revolutionary changes in traumatology. The purpose of this study is to provide a comprehensive overview of the current state of artificial intelligence in all aspects of traumatology, an in-depth analysis of its clinical value and limitations, as well as to offer practical recommendations based on the latest data. Through a systematic review of existing research, we hope to provide doctors, researchers, and policy makers with authoritative background information on the use of AI in traumatology.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>искусственный интеллект</kwd><kwd>травматология</kwd><kwd>диагностика травмы</kwd><kwd>лечение</kwd><kwd>прогнозы</kwd></kwd-group><kwd-group xml:lang="en"><kwd>artificial intelligence</kwd><kwd>traumatology</kwd><kwd>injury diagnosis</kwd><kwd>treatment</kwd><kwd>prognosis</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">Середа А.П., Джавадов А.А., Черный А.А. Искусственный интеллект в травматологии и ортопедии. Реальность, фантазии или обман? // Травматология и ортопедия России. – 2024. – Т.30. – №2. – С.181-191. doi: 10.17816/2311-2905-17468.</mixed-citation><mixed-citation xml:lang="en">Sereda AP, Dzhavadov AA, Chernyj AA. Artificial Intelligence in Traumatology and Orthopedics. Reality, Fantasy or Deception? Travmatologiya i ortopedia Rossii. 2024; 30(2): 181-191. (In Russ.) doi: 10.17816/2311-2905-17468.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017; 69: 36-40. doi: 10.1016/j.metabol.2017.01.011.</mixed-citation><mixed-citation xml:lang="en">Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017; 69: 36-40. doi: 10.1016/j.metabol.2017.01.011.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Тополь Э. Искусственный интеллект в медицине. Как умные технологии меняют подход к лечению. – М.: Альпина Диджитал, 2019.</mixed-citation><mixed-citation xml:lang="en">Topol E. Artificial intelligence in medicine. How smart technologies change the approach to treatment. М.: Alpina Digital Publ., 2019. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019; 28: 73-81. doi: 10.1080/13645706.2019.1575882.</mixed-citation><mixed-citation xml:lang="en">Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019; 28: 73-81. doi: 10.1080/13645706.2019.1575882.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke. 2017; 48: 1416-9. doi: 10.1161/STROKEAHA.116.016281.</mixed-citation><mixed-citation xml:lang="en">Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke. 2017; 48: 1416-9. doi: 10.1161/STROKEAHA.116.016281.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Mayo RC, Leung J. Artificial intelligence and deep learning – radiology’s next frontier? Clin Imaging. 2018; 49: 87-8. doi: 10.1016/j.clinimag.2017.11.007.</mixed-citation><mixed-citation xml:lang="en">Mayo RC, Leung J. Artificial intelligence and deep learning – radiology’s next frontier? Clin Imaging. 2018; 49: 87-8. doi: 10.1016/j.clinimag.2017.11.007.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Abujaber A, Fadlalla A, Gammoh D, Abdelrahman H, Mollazehi M, El-Menyar A. Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: machine learning approach. PLoS ONE. 2020; 15(7): e0235231.</mixed-citation><mixed-citation xml:lang="en">Abujaber A, Fadlalla A, Gammoh D, Abdelrahman H, Mollazehi M, El-Menyar A. Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: machine learning approach. PLoS ONE. 2020; 15(7): e0235231.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Ahmed FS, Ali L, Joseph BA, Ikram A, Ul Mustafa R, Bukhari SAC. A statistically rigorous deep neural-network approach to predict mortality in trauma patients admitted to the intensive-care unit. J Trauma Acute Care Surg. 2020; 89(4): 736-42.</mixed-citation><mixed-citation xml:lang="en">Ahmed FS, Ali L, Joseph BA, Ikram A, Ul Mustafa R, Bukhari SAC. A statistically rigorous deep neural-network approach to predict mortality in trauma patients admitted to the intensive-care unit. J Trauma Acute Care Surg. 2020; 89(4): 736-42.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Hale AT, Stonko DP, Lim J, Guillamondegui OD, Shannon CN, Patel MB. Using an artificial neural network to predict traumatic-brain-injury outcomes. J Neurosurg Pediatr. 2018; 23(2): 219-26.</mixed-citation><mixed-citation xml:lang="en">Hale AT, Stonko DP, Lim J, Guillamondegui OD, Shannon CN, Patel MB. Using an artificial neural network to predict traumatic-brain-injury outcomes. J Neurosurg Pediatr. 2018; 23(2): 219-26.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Matsuo K, Aihara H, Nakai T, Morishita A, Tohma Y, Kohmura E. Machine learning to predict in-hospital morbidity and mortality after traumatic brain injury. J Neurotrauma. 2020; 37(1): 202-10.</mixed-citation><mixed-citation xml:lang="en">Matsuo K, Aihara H, Nakai T, Morishita A, Tohma Y, Kohmura E. Machine learning to predict in-hospital morbidity and mortality after traumatic brain injury. J Neurotrauma. 2020; 37(1): 202-10.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Hunter OF, Perry F, Salehi M, et al. Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care. World J Emerg Surg. 2023; 18: 16. doi: 10.1186/s13017-022-00469-1.</mixed-citation><mixed-citation xml:lang="en">Hunter OF, Perry F, Salehi M, et al. Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care. World J Emerg Surg. 2023; 18: 16. doi: 10.1186/s13017-022-00469-1.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">AlMamlook RE, Kwayu KM, Alkasisbeh MR, Frefer AA. Comparison of machine learning algorithms for predicting traffic accident severity. In: IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). 2019: 272-6. doi: 10.1109/JEEIT.2019.8717393.</mixed-citation><mixed-citation xml:lang="en">AlMamlook RE, Kwayu KM, Alkasisbeh MR, Frefer AA. Comparison of machine learning algorithms for predicting traffic accident severity. In: IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). 2019: 272-6. doi: 10.1109/JEEIT.2019.8717393.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Bao J, Liu P, Ukkusuri SV. A spatiotemporal deep learning approach for city-wide short-term crash-risk prediction with multimodal data. Accid Anal Prev. 2019; 122: 239-54.</mixed-citation><mixed-citation xml:lang="en">Bao J, Liu P, Ukkusuri SV. A spatiotemporal deep learning approach for city-wide short-term crash-risk prediction with multimodal data. Accid Anal Prev. 2019; 122: 239-54.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Mansoor U, Ratrout NT, Rahman SM, Assi K. Crash severity prediction using two-layer ensemble machine learning model for proactive emergency management. IEEE Access. 2020; 8: 210750-62.</mixed-citation><mixed-citation xml:lang="en">Mansoor U, Ratrout NT, Rahman SM, Assi K. Crash severity prediction using two-layer ensemble machine learning model for proactive emergency management. IEEE Access. 2020; 8: 210750-62.</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Torres-Garcia AA, Reyes-García CA, Villaseñor-Pineda L, Mendoza-Montoya O, eds. Biosignal Processing and Classification Using Computational Learning and Intelligence. Academic Press. 2022: 111-29.</mixed-citation><mixed-citation xml:lang="en">Torres-Garcia AA, Reyes-García CA, Villaseñor-Pineda L, Mendoza-Montoya O, eds. Biosignal Processing and Classification Using Computational Learning and Intelligence. Academic Press. 2022: 111-29.</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Amiri AM, Sadri A, Nadimi N, Shams M. A comparison between artificial neural network and hybrid intelligent genetic algorithm in predicting the severity of fixed-object crashes among elderly drivers. Accid Anal Prev. 2020; 138: 105468.</mixed-citation><mixed-citation xml:lang="en">Amiri AM, Sadri A, Nadimi N, Shams M. A comparison between artificial neural network and hybrid intelligent genetic algorithm in predicting the severity of fixed-object crashes among elderly drivers. Accid Anal Prev. 2020; 138: 105468.</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Assi K. Prediction of traffic-crash-severity using deep neural networks: a comparative study. In: International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT). 2020: 1-6. doi: 10.1109/3ICT51146.2020.9311974.</mixed-citation><mixed-citation xml:lang="en">Assi K. Prediction of traffic-crash-severity using deep neural networks: a comparative study. In: International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT). 2020: 1-6. doi: 10.1109/3ICT51146.2020.9311974.</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Nederpelt CJ, Mokhtari AK, Alser O, Tsiligkaridis T, et al. Development of a field artificial-intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds. J Trauma Acute Care Surg. 2021; 90(6): 1054-60.</mixed-citation><mixed-citation xml:lang="en">Nederpelt CJ, Mokhtari AK, Alser O, Tsiligkaridis T, et al. Development of a field artificial-intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds. J Trauma Acute Care Surg. 2021; 90(6): 1054-60.</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">El Hechi MW, Maurer LR, Levine J, Zhuo D, et al. Validation of the artificial intelligence–based predictive optimal trees in emergency surgery risk (POTTER) calculator in emergency-general-surgery and emergency-laparotomy patients. J Am Coll Surg. 2021; 232(6): 912-9.</mixed-citation><mixed-citation xml:lang="en">El Hechi MW, Maurer LR, Levine J, Zhuo D, et al. Validation of the artificial intelligence–based predictive optimal trees in emergency surgery risk (POTTER) calculator in emergency-general-surgery and emergency-laparotomy patients. J Am Coll Surg. 2021; 232(6): 912-9.</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Gorczyca MT, Toscano NC, Cheng JD. The trauma-severity-model: An ensemble machine-learning approach to risk-prediction. Comput Biol Med. 2019; 108: 9-19.</mixed-citation><mixed-citation xml:lang="en">Gorczyca MT, Toscano NC, Cheng JD. The trauma-severity-model: An ensemble machine-learning approach to risk-prediction. Comput Biol Med. 2019; 108: 9-19.</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Shahi N, Shahi AK, Phillips R, Shirek G, et al. Decision-making in pediatricblunt-solid-organ-injury: A deep-learning approach to predict massivetransfusion, need-for-operative-management, and mortality-risk. J Pediatr Surg. 2021; 56(2): 379-84.</mixed-citation><mixed-citation xml:lang="en">Shahi N, Shahi AK, Phillips R, Shirek G, et al. Decision-making in pediatricblunt-solid-organ-injury: A deep-learning approach to predict massivetransfusion, need-for-operative-management, and mortality-risk. J Pediatr Surg. 2021; 56(2): 379-84.</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">He W, Fu X, Chen S. Advancing polytrauma care: developing and validating machine learning models for early mortality prediction. J Transl Med. 2023; 21: 664. doi: 10.1186/s12967-023-04487-8.</mixed-citation><mixed-citation xml:lang="en">He W, Fu X, Chen S. Advancing polytrauma care: developing and validating machine learning models for early mortality prediction. J Transl Med. 2023; 21: 664. doi: 10.1186/s12967-023-04487-8.</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Paydar S, Parva E, Ghahramani Z, Pourahmad S, et al. Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data-mining artificial intelligence. Chin J Traumatol. 2021; 24(1): 48-52.</mixed-citation><mixed-citation xml:lang="en">Paydar S, Parva E, Ghahramani Z, Pourahmad S, et al. Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data-mining artificial intelligence. Chin J Traumatol. 2021; 24(1): 48-52.</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Maurer LR, Bertsimas D, Bouardi HT, El Hechi M, et al. Trauma-outcomepredictor: An artificial-intelligence-interactive smartphone-tool to predict outcomes in trauma patients. J Trauma Acute Care Surg. 2021; 91(1): 93-9.</mixed-citation><mixed-citation xml:lang="en">Maurer LR, Bertsimas D, Bouardi HT, El Hechi M, et al. Trauma-outcomepredictor: An artificial-intelligence-interactive smartphone-tool to predict outcomes in trauma patients. J Trauma Acute Care Surg. 2021; 91(1): 93-9.</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">McCall HC, Richardson CG, Helgadottir FD, Chen FS. Evaluating a webbased social anxiety intervention: A randomized controlled trial among university students. J Med Internet Res. 2018; 20: e91. doi: 10.2196/jmir.8630.</mixed-citation><mixed-citation xml:lang="en">McCall HC, Richardson CG, Helgadottir FD, Chen FS. Evaluating a webbased social anxiety intervention: A randomized controlled trial among university students. J Med Internet Res. 2018; 20: e91. doi: 10.2196/jmir.8630.</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Sinsky C, Colligan L, Li L, Prgomet M, et al. Allocation of physician time in ambulatory practice: A time and motion study in four specialties. Ann Intern Med. 2016; 165: 753-60. doi: 10.7326/M16-0961.</mixed-citation><mixed-citation xml:lang="en">Sinsky C, Colligan L, Li L, Prgomet M, et al. Allocation of physician time in ambulatory practice: A time and motion study in four specialties. Ann Intern Med. 2016; 165: 753-60. doi: 10.7326/M16-0961.</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Cheng C-Y, Chiu I-M, Hsu M-Y, Pan H-Y, et al. Deep learning-assisted detection of abdominal free fluid in Morison’s pouch during focused assessment with sonography in trauma. Front Med. 2021; 8: 707437.</mixed-citation><mixed-citation xml:lang="en">Cheng C-Y, Chiu I-M, Hsu M-Y, Pan H-Y, et al. Deep learning-assisted detection of abdominal free fluid in Morison’s pouch during focused assessment with sonography in trauma. Front Med. 2021; 8: 707437.</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Rashidi HH, Sen S, Palmieri TL, Blackmon T, et al. Early recognition of burn-and-trauma-related acute-kidney-injury: A pilot-comparison-of-machine-learning-techniques. Scientific Reports. 2020; 10(1): 205-6.</mixed-citation><mixed-citation xml:lang="en">Rashidi HH, Sen S, Palmieri TL, Blackmon T, et al. Early recognition of burn-and-trauma-related acute-kidney-injury: A pilot-comparison-of-machine-learning-techniques. Scientific Reports. 2020; 10(1): 205-6.</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Stonko DP, Dennis BM, Betzold RD, Peetz AB, Gunter OL, Guillamondegui OD. Artificial intelligence can predict daily trauma volume and average acuity. J Trauma Acute Care Surg. 2018; 85(2): 393-7.</mixed-citation><mixed-citation xml:lang="en">Stonko DP, Dennis BM, Betzold RD, Peetz AB, Gunter OL, Guillamondegui OD. Artificial intelligence can predict daily trauma volume and average acuity. J Trauma Acute Care Surg. 2018; 85(2): 393-7.</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Corban J, Lorange JP, Laverdiere C, Khoury J, et al. Artificial intelligence in the management of anterior cruciate ligament injuries. Orthop J Sports Med. 2021; 9(7): 23259671211014206. doi: 10.1177/23259671211014206.</mixed-citation><mixed-citation xml:lang="en">Corban J, Lorange JP, Laverdiere C, Khoury J, et al. Artificial intelligence in the management of anterior cruciate ligament injuries. Orthop J Sports Med. 2021; 9(7): 23259671211014206. doi: 10.1177/23259671211014206.</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Staziaki PV, Wu D, Rayan JC, Santo IDO, et al. Machine learning combining CT-findings and clinical-parameters improves prediction of length-of-stay and ICU-admission in torso-trauma. Eur Radiol. 2021; 31(7): 5434-41.</mixed-citation><mixed-citation xml:lang="en">Staziaki PV, Wu D, Rayan JC, Santo IDO, et al. Machine learning combining CT-findings and clinical-parameters improves prediction of length-of-stay and ICU-admission in torso-trauma. Eur Radiol. 2021; 31(7): 5434-41.</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">Worldwide Antimicrobial Resistance National/International Network Group (WARNING) Collaborators. Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action. World J Emerg Surg. 2023; 18: 50. doi: 10.1186/s13017-023-00518-3.</mixed-citation><mixed-citation xml:lang="en">Worldwide Antimicrobial Resistance National/International Network Group (WARNING) Collaborators. Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action. World J Emerg Surg. 2023; 18: 50. doi: 10.1186/s13017-023-00518-3.</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">Lisacek-Kiosoglous AB, Powling AS, Fontalis A, Gabr A, et al. Artificial intelligence in orthopaedic surgery. Bone Joint Res. 2023; 12(7): 447-54. doi: 10.1302/2046-3758.127.BJR-2023-0111.R1.</mixed-citation><mixed-citation xml:lang="en">Lisacek-Kiosoglous AB, Powling AS, Fontalis A, Gabr A, et al. Artificial intelligence in orthopaedic surgery. Bone Joint Res. 2023; 12(7): 447-54. doi: 10.1302/2046-3758.127.BJR-2023-0111.R1.</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">Zhang X, Zhang D, Zhang X, Zhang X. Artificial intelligence applications in the diagnosis and treatment of bacterial infections. Front Microbiol. 2024; 15: 1449844. doi: 10.3389/fmicb.2024.1449844.</mixed-citation><mixed-citation xml:lang="en">Zhang X, Zhang D, Zhang X, Zhang X. Artificial intelligence applications in the diagnosis and treatment of bacterial infections. Front Microbiol. 2024; 15: 1449844. doi: 10.3389/fmicb.2024.1449844.</mixed-citation></citation-alternatives></ref><ref id="cit35"><label>35</label><citation-alternatives><mixed-citation xml:lang="ru">Nourelahi M, Dadboud F, Khalili H, Niakan A, Parsaei H. A machine-learning model for predicting favorable outcome in severe-traumatic-brain-injury patients after six-month follow-up. Acute Crit Care. 2022; 37: 45-52.</mixed-citation><mixed-citation xml:lang="en">Nourelahi M, Dadboud F, Khalili H, Niakan A, Parsaei H. A machine-learning model for predicting favorable outcome in severe-traumatic-brain-injury patients after six-month follow-up. Acute Crit Care. 2022; 37: 45-52.</mixed-citation></citation-alternatives></ref><ref id="cit36"><label>36</label><citation-alternatives><mixed-citation xml:lang="ru">Rau CS, Wu SC, Chuang JF, Huang CY, Liu HT, Chien PC, et al. Machine-learning models of survival prediction in trauma patients. Journal of Clinical Medicine. 2019; 8(6): 799. doi: 10.3390/jcm8060799.</mixed-citation><mixed-citation xml:lang="en">Rau CS, Wu SC, Chuang JF, Huang CY, Liu HT, Chien PC, et al. Machine-learning models of survival prediction in trauma patients. Journal of Clinical Medicine. 2019; 8(6): 799. doi: 10.3390/jcm8060799.</mixed-citation></citation-alternatives></ref><ref id="cit37"><label>37</label><citation-alternatives><mixed-citation xml:lang="ru">Kurmis AP, Ianunzio JR. Artificial intelligence in orthopedic surgery: Evolution, current state, and future directions. Arthroplasty. 2022; 4(1): 9. doi: 10.1186/s42836-022-00112-z.</mixed-citation><mixed-citation xml:lang="en">Kurmis AP, Ianunzio JR. Artificial intelligence in orthopedic surgery: Evolution, current state, and future directions. Arthroplasty. 2022; 4(1): 9. doi: 10.1186/s42836-022-00112-z.</mixed-citation></citation-alternatives></ref><ref id="cit38"><label>38</label><citation-alternatives><mixed-citation xml:lang="ru">El Hechi M, Gebran A, Bouardi HT, Maurer LR, et al. Validation of the artificial-intelligence-based trauma-outcomes-predictor (TOP) in patients aged ≥65 years. Surgery. 2022; 171(6): 1687-94.</mixed-citation><mixed-citation xml:lang="en">El Hechi M, Gebran A, Bouardi HT, Maurer LR, et al. Validation of the artificial-intelligence-based trauma-outcomes-predictor (TOP) in patients aged ≥65 years. Surgery. 2022; 171(6): 1687-94.</mixed-citation></citation-alternatives></ref><ref id="cit39"><label>39</label><citation-alternatives><mixed-citation xml:lang="ru">Innocenti B, Radyul Y, Bori E. The use of artificial intelligence in orthopedics: Applications and limitations of machine learning in diagnosis and prediction. Applied Sciences. 2022; 12(21): 10775. doi: 10.3390/app122110775.</mixed-citation><mixed-citation xml:lang="en">Innocenti B, Radyul Y, Bori E. The use of artificial intelligence in orthopedics: Applications and limitations of machine learning in diagnosis and prediction. Applied Sciences. 2022; 12(21): 10775. doi: 10.3390/app122110775.</mixed-citation></citation-alternatives></ref><ref id="cit40"><label>40</label><citation-alternatives><mixed-citation xml:lang="ru">Kumar V, Patel S, Baburaj V, Vardhan A, et al. Current understanding on artificial intelligence and machine learning in orthopaedics – A scoping review. J Orthop. 2022; 34: 201-6. doi: 10.1016/j.jor.2022.08.020.</mixed-citation><mixed-citation xml:lang="en">Kumar V, Patel S, Baburaj V, Vardhan A, et al. Current understanding on artificial intelligence and machine learning in orthopaedics – A scoping review. J Orthop. 2022; 34: 201-6. doi: 10.1016/j.jor.2022.08.020.</mixed-citation></citation-alternatives></ref><ref id="cit41"><label>41</label><citation-alternatives><mixed-citation xml:lang="ru">Masters K. Artificial intelligence in medical education. Med Teacher. 2019; 41(9): 976-980.</mixed-citation><mixed-citation xml:lang="en">Masters K. Artificial intelligence in medical education. Med Teacher. 2019; 41(9): 976-980.</mixed-citation></citation-alternatives></ref><ref id="cit42"><label>42</label><citation-alternatives><mixed-citation xml:lang="ru">Katznelson G, Gerke S. The need for health AI ethics in medical school education. AdvHealthSciEducTheoryPract. 2021; 26(4): 1447-1458.</mixed-citation><mixed-citation xml:lang="en">Katznelson G, Gerke S. The need for health AI ethics in medical school education. AdvHealthSciEducTheoryPract. 2021; 26(4): 1447-1458.</mixed-citation></citation-alternatives></ref><ref id="cit43"><label>43</label><citation-alternatives><mixed-citation xml:lang="ru">Гажва С.И., Горбатов Р.О., Ююрихина М.Н., Тетерин А.И., Янышева К.Л. 3D-технологии в медицине // Аддитивные технологии. – 2023. – №2. – С.70-77.</mixed-citation><mixed-citation xml:lang="en">Gazhva SI, Gorbatov RO, Yuryukhina MN, Teteryn AI, Yanisheva KL. 3D Technologies in Medicine. Additive Technologies. 2023; 2: 70-77. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="cit44"><label>44</label><citation-alternatives><mixed-citation xml:lang="ru">Park SH, Do KH, Kim S, et.al. What Should Medical Students Know About Artificial Intelligence in Medicine? Educ Eval Health Prof. 2019; 16: 16-21.</mixed-citation><mixed-citation xml:lang="en">Park SH, Do KH, Kim S, et.al. What Should Medical Students Know About Artificial Intelligence in Medicine? Educ Eval Health Prof. 2019; 16: 16-21.</mixed-citation></citation-alternatives></ref><ref id="cit45"><label>45</label><citation-alternatives><mixed-citation xml:lang="ru">Кошечкин К.А., Хохлов А.Л. Этические проблемы внедрения искусственного интеллекта в здравоохранении // Медицинская этика. – 2024. – №1. – С.12-19. doi: 10.24075/medet.2024.006.</mixed-citation><mixed-citation xml:lang="en">Koshechkin KA, Khokhlov AL. Ethical issues of artificial intelligence implementation in healthcare. Meditsinskaya etika. 2024; 1: 12-19. (In Russ.) doi: 10.24075/medet.2024.006.</mixed-citation></citation-alternatives></ref><ref id="cit46"><label>46</label><citation-alternatives><mixed-citation xml:lang="ru">Хайдарова Н.Т.К. Конфиденциальность и защита данных с учетом применения искусственного интеллекта в рабочих процессах // Central Asian Journal of Education and Innovation. – 2024. – Т.3. – №5-3. – С.137-141. doi: 10.5281/zenodo.11408148.</mixed-citation><mixed-citation xml:lang="en">Haydарова NTK. Konfidentsial’nost’ i zashchita dannykh s uchetom primeneniya iskusstvennogo intellekta v rabochih protsessah. Central Asian Journal of Education and Innovation. 2024; 3(5-3): 137-141. (In Russ.) doi: 10.5281/zenodo.11408148.</mixed-citation></citation-alternatives></ref><ref id="cit47"><label>47</label><citation-alternatives><mixed-citation xml:lang="ru">Cheng K, Guo Q, He Y, Lu Y, et al. Artificial intelligence in sports medicine: Could GPT-4 make human doctors obsolete? Ann Biomed Eng. 2023; 51(8): 1658-62. doi: 10.1007/s10439-023-03213-1.</mixed-citation><mixed-citation xml:lang="en">Cheng K, Guo Q, He Y, Lu Y, et al. Artificial intelligence in sports medicine: Could GPT-4 make human doctors obsolete? Ann Biomed Eng. 2023; 51(8): 1658-62. doi: 10.1007/s10439-023-03213-1.</mixed-citation></citation-alternatives></ref><ref id="cit48"><label>48</label><citation-alternatives><mixed-citation xml:lang="ru">Мельников А.А. Потенциальная ответственность врачей, использующих искусственный интеллект // Дальневосточный медицинский журнал. – 2024. – №1. – С.77-80. doi: 10.35177/1994-5191-2024-1-13.</mixed-citation><mixed-citation xml:lang="en">Melnikov AA. Potential liability of doctors using artificial intelligence. Dal’nevostochnyy meditsinskiy zhurnal. 2024; 1: 77-80. (In Russ.) doi: 10.35177/1994-5191-2024-1-13.</mixed-citation></citation-alternatives></ref><ref id="cit49"><label>49</label><citation-alternatives><mixed-citation xml:lang="ru">Ghantasala GS, Dilip K, Vidyullatha P, et al. Enhanced ovarian cancer survival prediction using temporal analysis and graph neural networks. BMC Med Inform Decis Mak. 2024; 24: 299. doi: 10.1186/s12911-024-02665-2.</mixed-citation><mixed-citation xml:lang="en">Ghantasala GS, Dilip K, Vidyullatha P, et al. Enhanced ovarian cancer survival prediction using temporal analysis and graph neural networks. BMC Med Inform Decis Mak. 2024; 24: 299. doi: 10.1186/s12911-024-02665-2.</mixed-citation></citation-alternatives></ref><ref id="cit50"><label>50</label><citation-alternatives><mixed-citation xml:lang="ru">Gyftopoulos S, Lin D, Knoll F, Doshi AM, Rodrigues TC, Recht MP. Artificial intelligence in musculoskeletal imaging: Current status and future directions. AJR Am J Roentgenol. 2019; 213(3): 506-13. doi: 10.2214/AJR.19.21117.</mixed-citation><mixed-citation xml:lang="en">Gyftopoulos S, Lin D, Knoll F, Doshi AM, Rodrigues TC, Recht MP. Artificial intelligence in musculoskeletal imaging: Current status and future directions. AJR Am J Roentgenol. 2019; 213(3): 506-13. doi: 10.2214/AJR.19.21117.</mixed-citation></citation-alternatives></ref><ref id="cit51"><label>51</label><citation-alternatives><mixed-citation xml:lang="ru">Шадеркин И.А. Роль искусственного интеллекта в телемедицине России // Журнал телемедицины и электронного здравоохранения. – 2019. – Т.5. – №1. – С.38-40. doi: 10.29188/2542-2413-2019-5-1-38-40.</mixed-citation><mixed-citation xml:lang="en">Shaderkin IA. The role of artificial intelligence in telemedicine in Russia. Zhurnal telemeditsiny i elektronnogo zdravookhraneniya. 2019; 5(1): 38-40. (In Russ.) doi: 10.29188/2542-2413-2019-5-1-38-40.</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>
