<?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="en"><front><journal-meta><journal-id journal-id-type="publisher-id">farmaec</journal-id><journal-title-group><journal-title xml:lang="en">FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology</journal-title><trans-title-group xml:lang="ru"><trans-title>ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2070-4909</issn><issn pub-type="epub">2070-4933</issn><publisher><publisher-name>IRBIS LLC</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.17749/2070-4909/farmakoekonomika.2025.328</article-id><article-id custom-type="elpub" pub-id-type="custom">farmaec-1240</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="en"><subject>ORIGINAL ARTICLES</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ОРИГИНАЛЬНЫЕ ПУБЛИКАЦИИ</subject></subj-group></article-categories><title-group><article-title>Artificial intelligence in healthcare: global implementation, legal regulation, problems and ethical issues</article-title><trans-title-group xml:lang="ru"><trans-title>Искусственный интеллект в здравоохранении: мировой опыт внедрения, правовое регулирование, проблемы и этические аспекты</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0459-0488</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Корабельников</surname><given-names>Д. И.</given-names></name><name name-style="western" xml:lang="en"><surname>Korabelnikov</surname><given-names>D. I.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Корабельников Даниил Иванович, к.м.н., доцент </p><p>2-я Брестская ул., д. 5, Москва 123056</p></bio><bio xml:lang="en"><p>5 2nd Brestskaya Str., Moscow 123056</p></bio><email xlink:type="simple">lamotkin.an@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-7930-6018</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Ламоткин</surname><given-names>А. И.</given-names></name><name name-style="western" xml:lang="en"><surname>Lamotkin</surname><given-names>A. I.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Ламоткин Андрей Игоревич </p><p>2-я Брестская ул., д. 5, Москва 123056; ул. Добролюбова, д. 11, Москва 127254</p></bio><bio xml:lang="en"><p>5 2nd Brestskaya Str., Moscow 123056;11 Dobrolyubov Str., Moscow 127254</p></bio><email xlink:type="simple">lamotkin.an@mail.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Автономная некоммерческая организация дополнительного профессионального образования «Московский медико-социальный институт им. Ф.П. Гааза»<country>Россия</country></aff><aff xml:lang="en">Moscow Haass Medical and Social Institute<country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru">Автономная некоммерческая организация дополнительного профессионального образования «Московский медико-социальный институт им. Ф.П. Гааза; Федеральное государственное бюджетное учреждение «Центральный научно-исследовательский институт организации и информатизации здравоохранения» Министерства здравоохранения Российской Федерации<country>Россия</country></aff><aff xml:lang="en">Moscow Haass Medical and Social Institute; Central Research Institute of Organization and Informatization of Healthcare<country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2023</year></pub-date><pub-date pub-type="epub"><day>11</day><month>09</month><year>2025</year></pub-date><volume>0</volume><issue>0</issue><issue-title>Online First</issue-title><elocation-id>1240</elocation-id><permissions><copyright-statement>Copyright &amp;#x00A9; Korabelnikov D.I., Lamotkin A.I., 2023</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="ru">Корабельников Д.И., Ламоткин А.И.</copyright-holder><copyright-holder xml:lang="en">Korabelnikov D.I., Lamotkin A.I.</copyright-holder><license 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.pharmacoeconomics.ru/jour/article/view/1240">https://www.pharmacoeconomics.ru/jour/article/view/1240</self-uri><abstract><sec><title>Objective</title><p>Objective: To analyze the legal and ethical aspects of regulating artificial intelligence (AI) in medicine in key jurisdictions (United States, European Union, China, Russia), to identify regulatory gaps, ethical dilemmas and prospects for harmonization of standards.</p></sec><sec><title>Material and methods</title><p>Material and methods. National and international regulatory documents (GDPR, AI Act, FDA, NMPA), scientific publications, clinical cases and regulatory initiatives (IMDRF, WHO) were reviewed. Methods for comparative legal analysis and systematization of ethical and legal norms were used.</p></sec><sec><title>Results</title><p>Results. Considerable differences in approaches to AI regulation were identified, including flexibility in the US, the ethical centricity in the EU, centralization in China and an emerging framework in Russia. Key issues were emphasized, such as algorithmic bias, AI transparency, responsibility, and the conflict between innovation and security.</p></sec><sec><title>Conclusion</title><p>Conclusion. The harmonization of international standards, the introduction of dynamic regulation and the strengthening of interdisciplinary cooperation should be pursued to achieve a balance between innovation and the protection of patients' rights.</p></sec></abstract><trans-abstract xml:lang="ru"><sec><title>Цель</title><p>Цель: анализ внедрения, правового регулирования, проблем и этических аспектов искусственного интеллекта (ИИ) в медицине в ключевых регионах (Соединенные Штаты Америки, Европейский союз, Китай, Россия), выявление регуляторных пробелов, этических дилемм и перспектив гармонизации стандартов.</p></sec><sec><title>Материалы и методы</title><p>Материалы и методы. Проведен обзор международных и национальных нормативных документов (GDPR, AI Act, FDA, NMPA), научных публикаций, клинических примеров и регуляторных инициатив (IMDRF, WHO). Использованы методы сравнительного правового анализа и систематизации этико-правовых норм.</p></sec><sec><title>Результаты</title><p>Результаты. Выявлены значительные различия в подходах к регулированию ИИ: гибкость в США, этикоцентричность в ЕС, централизация в Китае и формирующаяся база в России. Обозначены ключевые проблемы: предвзятость алгоритмов, прозрачность ИИ, ответственность, конфликт инноваций и безопасности.</p></sec><sec><title>Заключение</title><p>Заключение. Необходима гармонизация международных стандартов, внедрение динамического регулирования и усиление междисциплинарного сотрудничества для баланса между инновациями и защитой прав пациентов.</p></sec></trans-abstract><kwd-group xml:lang="ru"><kwd>искусственный интеллект</kwd><kwd>медицинское право</kwd><kwd>правовое регулирование</kwd><kwd>этика</kwd><kwd>предвзятость алгоритмов</kwd><kwd>конфиденциальность данных</kwd></kwd-group><kwd-group xml:lang="en"><kwd>artificial intelligence</kwd><kwd>medical law</kwd><kwd>legal regulation</kwd><kwd>ethics</kwd><kwd>algorithmic bias</kwd><kwd>data privacy</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">Schulz W.L, Durant T.J.S, Krumholz H.M. Validation and regulation of clinical artificial intelligence. Clin Chem. 2019; 65 (10): 1336–7. https://doi.org/10.1373/clinchem.2019.308304.</mixed-citation><mixed-citation xml:lang="en">Behara K., Bhero E., Agee J.T. AI in dermatology: a comprehensive review into skin cancer detection. Peer J Comput Sci. 2024; 10: e2530. https://doi.org/10.7717/peerj-cs.2530.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Drabiak K., Kyzer S., Nemov V., El Naqa I. AI and machine learning ethics, law, diversity, and global impact. Br J Radiol. 2023; 96 (1150): 20220934. https://doi.org/10.1259/bjr.20220934.</mixed-citation><mixed-citation xml:lang="en">Sangers T.E., Wakkee M., Moolenburgh F.J., et al. Towards successful implementation of artificial intelligence in skin cancer care: a qualitative study exploring the views of dermatologists and general practitioners. Arch Dermatol Res. 2023; 315 (5): 1187–95. https://doi.org/10.1007/s00403-022-02492-3.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">World Health Organization. Ethics and governance of artificial intelligence for health: guidance on large multi-modal models. Available at: https://www.who.int/publications/i/item/9789240084759 (accessed 21.06.2025).</mixed-citation><mixed-citation xml:lang="en">Lamotkin A.I., Korabelnikov D.I., Olisova O.Yu., Lamotkin I.A. Patient routing algorithm when using the artificial intelligence program “Derma Onko Check” for differential diagnosis of skin neoplasms. Current Problems of Health Care and Medical Statistics. 2025; 5: 139–59 (in Russ.). https://doi.org/10.24412/2312-2935-2025-5-139-159.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">UNESCO. Recommendation on the ethics of artificial intelligence. Available at: https://www.unesco.org/en/articles/recommendation-ethics-artificial-intelligence (accessed 21.06.2025).</mixed-citation><mixed-citation xml:lang="en">Korabelnikov D.I., Lamotkin A.I. Patient routing algorithm in differential diagnosis of cutaneous neoplasms with the combined use of Derma Onko Check and Melanoma Check artificial intelligence software tools. Clinical Review for General Practice. 2025; 6 (12): 71–9 (in Russ.). https://doi.org/10.47407/kr2025.6.11.00715.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016. Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R0679 (accessed 21.06.2025).</mixed-citation><mixed-citation xml:lang="en">Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A. Patient routing algorithm for differential diagnosis of skin neoplasms using the artificial intelligence program “Melanoma Check”. Medical Bulletin of the Main Military Cinical Hospital named after N.N. Burdenko. 2025; 4: 6–13 (in Russ.). https://doi.org/10.53652/2782-1730-2025-6-4-06-13.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">IMDRF (International Medical Device Regulators Forum). Software as a medical device (SaMD): key definitions. Available at: https://www.imdrf.org/sites/default/files/2022-08/IMDRF%20SaMD%20WG%20N67FINAL2022.pdf (accessed 21.06.2025).</mixed-citation><mixed-citation xml:lang="en">Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A. Preliminary differential diagnosis of benign and malignant tumors from epidermal skin tissue using an artificial intelligence program “Derma Onko Check”. Current Problems of Health Care and Medical Statistics. 2025; 2: 223–42 (in Russ.). https://doi.org/10.24412/2312-2935-2025-2-223-242.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">World Health Organization. Global Strategy on Digital Health 2020–2025. Available at: https://www.who.int/publications/i/item/9789240020924 (accessed 21.06.2025).</mixed-citation><mixed-citation xml:lang="en">Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A., et al. Melanoma check the accuracy of the preliminary diagnosis of malignant melanocytic skin tumors using the artificial intelligence program “Melanoma Check”. Medical Bulletin of the Main Military Cinical Hospital named after N.N. Burdenko. 2025; 1: 42–51 (in Russ.). https://doi.org/10.53652/2782-1730-2025-6-1-42-51.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">FDA draft guidance: marketing submission recommendations for a predetermined change control plan for artificial intelligence/machine learning (AI/ML)-enabled device software functions. Available at: https://www.fda.gov/media/166704/download (accessed 21.06.2025).</mixed-citation><mixed-citation xml:lang="en">De Bedout V., Williams N.M., Muñoz A.M., et al. Skin cancer and dermoscopy training for primary care physicians: a pilot study. Dermatol Pract Concept. 2021; 11 (1): e2021145. https://doi.org/10.5826/dpc.1101a145.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Hoffman R.R., Mueller S.T., Klein G., Litman J. Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance. Front Comput Sci. 2023; 5. https://doi.org/10.3389/fcomp.2023.1096257.</mixed-citation><mixed-citation xml:lang="en">Neretin E.Yu., Titov K.S., Zapirov G.M. Primary early diagnosis of skin melanoma after individual training of doctors. Russian Journal of Clinical Dermatology and Venereology. 2023; 22 (1): 99–105 (in Russ.). https://doi.org/10.17116/klinderma20232201199.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (Text with EEA relevance). Available at: https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng (accessed 21.06.2025).</mixed-citation><mixed-citation xml:lang="en">Chen J.Y., Fernandez K., Fadadu R.P., et al. Skin cancer diagnosis by lesion, physician, and examination type: a systematic review and meta-analysis. JAMA Dermatol. 2025; 161 (2): 135–46. https://doi.org/10.1001/jamadermatol.2024.4382.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Duffourc M.N., Gerke S. Health care AI and patient privacy – Dinerstein v Google. JAMA. 2024; 331 (11): 909–10. https://doi.org/10.1001/jama.2024.1110.</mixed-citation><mixed-citation xml:lang="en">Omelyanovskiy V.V., Gorkavenko F.V., Ryagina V.A., et al. Basic approaches to assessment of digital health products and services in the Russian Federation. FARMAKOEKONOMIKA. Sovremennaya farmakoekonomika i farmakoepidemiologiya / FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2025; 18 (4): 473–82 (in Russ.). https://doi.org/10.17749/2070-4909/farmakoekonomika.2025.349.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Wong A., Otles E., Donnelly J.P., et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021; 181 (8): 1065–70. https://doi.org/10.1001/jamainternmed.2021.2626.</mixed-citation><mixed-citation xml:lang="en">Babenko A.I., Pushkarev O.V. Methodological basis of the complex estimation of medical-economic efficiency of public health services. Bulletin of the Siberian Branch of the Russian Academy of Medical Sciences. 2014; 34 (2): 89–94 (in Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Wang C., Zhang J., Lassi N., Zhang X. Privacy protection in using artificial intelligence for healthcare: chinese regulation in comparative perspective. Healthcare. 2022; 10 (10): 1878. https://doi.org/10.3390/healthcare10101878.</mixed-citation><mixed-citation xml:lang="en">Krasnova L.S., Arkova E.S., Luchinin E.A., Kholovnya-Voloskova M.A. Methodological recommendations for organizing and conducting clinical and economic analysis of medical devices. Мoscow: Research Institute of Healthcare Organization and Medical Management; 2022: 60 pp. (in Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Указ Президента РФ от 10.10.2019 № 490 «О развитии искусственного интеллекта в Российской Федерации». URL: https://base.garant.ru/72838946/?ysclid=mcujb5g0yf582538441 (дата обращения 23.06.2023).</mixed-citation><mixed-citation xml:lang="en">Указ Президента РФ от 10.10.2019 № 490 «О развитии искусственного интеллекта в Российской Федерации». URL: https://base.garant.ru/72838946/?ysclid=mcujb5g0yf582538441 (дата обращения 23.06.2023).</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Федеральный закон от 31.07.2020 № 258-ФЗ «Об экспериментальных правовых режимах в сфере цифровых инноваций в Российской Федерации». URL: https://base.garant.ru/74451176/?ysclid=mcujfao162823696623 (дата обращения 23.06.2023).</mixed-citation><mixed-citation xml:lang="en">Федеральный закон от 31.07.2020 № 258-ФЗ «Об экспериментальных правовых режимах в сфере цифровых инноваций в Российской Федерации». URL: https://base.garant.ru/74451176/?ysclid=mcujfao162823696623 (дата обращения 23.06.2023).</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Федеральный закон от 21.11.2011 № 323-ФЗ «Об основах охраны здоровья граждан в Российской Федерации». URL: https://base.garant.ru/12191967/?ysclid=mcujitz7di937678356 (дата обращения 23.06.2023).</mixed-citation><mixed-citation xml:lang="en">Федеральный закон от 21.11.2011 № 323-ФЗ «Об основах охраны здоровья граждан в Российской Федерации». URL: https://base.garant.ru/12191967/?ysclid=mcujitz7di937678356 (дата обращения 23.06.2023).</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Постановление Правительства РФ от 30.11.2024 № 1684 «Об утверждении правил государственной регистрации медицинских изделий». URL: https://www.garant.ru/products/ipo/prime/doc/410924190/?ysclid=mcujo7ypgi543807069 (дата обращения 23.06.2023).</mixed-citation><mixed-citation xml:lang="en">Постановление Правительства РФ от 30.11.2024 № 1684 «Об утверждении правил государственной регистрации медицинских изделий». URL: https://www.garant.ru/products/ipo/prime/doc/410924190/?ysclid=mcujo7ypgi543807069 (дата обращения 23.06.2023).</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Приказ Министерства здравоохранения РФ от 6.06.2012 № 4н «Об утверждении номенклатурной классификации медицинских изделий». URL: https://base.garant.ru/70199586/ (дата обращения 23.06.2023).</mixed-citation><mixed-citation xml:lang="en">Приказ Министерства здравоохранения РФ от 6.06.2012 № 4н «Об утверждении номенклатурной классификации медицинских изделий». URL: https://base.garant.ru/70199586/ (дата обращения 23.06.2023).</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Кодекс этики применения искусственного интеллекта в сфере охраны здоровья. Версия 2.1 (утв. Межведомственной рабочей группой при Минздраве России по вопросам создания, развития и внедрения в клиническую практику медицинских изделий и сервисов с использованием технологий искусственного интеллекта, протокол от 14 февраля 2025 г. N 90/18-0/117). URL: https://www.garant.ru/products/ipo/prime/doc/411615533/?ysclid=mcujwgpzg7498477087 (дата обращения 23.06.2023).</mixed-citation><mixed-citation xml:lang="en">Кодекс этики применения искусственного интеллекта в сфере охраны здоровья. Версия 2.1 (утв. Межведомственной рабочей группой при Минздраве России по вопросам создания, развития и внедрения в клиническую практику медицинских изделий и сервисов с использованием технологий искусственного интеллекта, протокол от 14 февраля 2025 г. N 90/18-0/117). URL: https://www.garant.ru/products/ipo/prime/doc/411615533/?ysclid=mcujwgpzg7498477087 (дата обращения 23.06.2023).</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Балтутите И.В. Правовые проблемы использования искусственного интеллекта в сфере здравоохранения. Правовая парадигма. 2022; 21 (2): 140–8. https://doi.org/10.15688/lc.jvolsu.2022.2.18.</mixed-citation><mixed-citation xml:lang="en">Балтутите И.В. Правовые проблемы использования искусственного интеллекта в сфере здравоохранения. Правовая парадигма. 2022; 21 (2): 140–8. https://doi.org/10.15688/lc.jvolsu.2022.2.18.</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Caruana R., Lou Y., Gehrke J., et al. Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015: 1721–30. https://doi.org/10.1145/2783258.2788613.</mixed-citation><mixed-citation xml:lang="en">Caruana R., Lou Y., Gehrke J., et al. Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015: 1721–30. https://doi.org/10.1145/2783258.2788613.</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD). Available at: https://www.fda.gov/media/122535/download (accessed 18.06.2025).</mixed-citation><mixed-citation xml:lang="en">Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD). Available at: https://www.fda.gov/media/122535/download (accessed 18.06.2025).</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Esteva A., Kuprel B., Novoa R.A., et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542: 115–8. https://doi.org/10.1038/nature21056.</mixed-citation><mixed-citation xml:lang="en">Esteva A., Kuprel B., Novoa R.A., et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542: 115–8. https://doi.org/10.1038/nature21056.</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Ribeiro M.T., Singh S., Guestrin C. “Why should i trust you?”: explaining the predictions of any classifier. arXiv:1602.04938. https://doi.org/10.48550/arXiv.1602.04938.</mixed-citation><mixed-citation xml:lang="en">Ribeiro M.T., Singh S., Guestrin C. “Why should i trust you?”: explaining the predictions of any classifier. arXiv:1602.04938. https://doi.org/10.48550/arXiv.1602.04938.</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">International Medical Device Regulators ForumGood machine learning practice for medical device development: guiding principles. Available at: https://www.imdrf.org/documents/good-machine-learning-practice-medical-device-development-guiding-principles (accessed 21.06.2025).</mixed-citation><mixed-citation xml:lang="en">International Medical Device Regulators ForumGood machine learning practice for medical device development: guiding principles. Available at: https://www.imdrf.org/documents/good-machine-learning-practice-medical-device-development-guiding-principles (accessed 21.06.2025).</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Health insurance portability and accountability act. 1996 Available at: https://www.congress.gov/bill/104th-congress/house-bill/3103/text (accessed 18.06.2025).</mixed-citation><mixed-citation xml:lang="en">Health insurance portability and accountability act. 1996 Available at: https://www.congress.gov/bill/104th-congress/house-bill/3103/text (accessed 18.06.2025).</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Lyell D., Wang Y., Coiera E., Magrabi F. More than algorithms: an analysis of safety events involving ML-enabled medical devices reported to the FDA. J Am Med Inform Assoc. 2023; 30 (7): 1227–36. https://doi.org/10.1093/jamia/ocad065.</mixed-citation><mixed-citation xml:lang="en">Lyell D., Wang Y., Coiera E., Magrabi F. More than algorithms: an analysis of safety events involving ML-enabled medical devices reported to the FDA. J Am Med Inform Assoc. 2023; 30 (7): 1227–36. https://doi.org/10.1093/jamia/ocad065.</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Froomkin M., Kerr I., Pineau J. When AIS outperform doctors: confronting the challenges of a Tort-Induced Over-Reliance on machine learning. Arizona Law Rev. 2019; 61: 33–99. http://doi.org/10.2139/ssrn.3114347.</mixed-citation><mixed-citation xml:lang="en">Froomkin M., Kerr I., Pineau J. When AIS outperform doctors: confronting the challenges of a Tort-Induced Over-Reliance on machine learning. Arizona Law Rev. 2019; 61: 33–99. http://doi.org/10.2139/ssrn.3114347.</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Software as a medical device (SaMD). International Medical Device Regulators Forum. Available at: https://www.fda.gov/media/100714/download (accessed 19.06.2025).</mixed-citation><mixed-citation xml:lang="en">Software as a medical device (SaMD). International Medical Device Regulators Forum. Available at: https://www.fda.gov/media/100714/download (accessed 19.06.2025).</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Digital health software precertification (Pre-Cert) pilot program. Available at: https://www.fda.gov/medical-devices/digital-health-center-excellence/digital-health-software-precertification-pre-cert-pilot-program (accessed 19.06.2025).</mixed-citation><mixed-citation xml:lang="en">Digital health software precertification (Pre-Cert) pilot program. Available at: https://www.fda.gov/medical-devices/digital-health-center-excellence/digital-health-software-precertification-pre-cert-pilot-program (accessed 19.06.2025).</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Wachter S., Mittelstadt B., Floridi L. Why a right to explanation of automated decision-making does not exist in the general data protection regulation. SSRN Electronic Journal. 2016. http://doi.org/10.1093/idpl/ipx005.</mixed-citation><mixed-citation xml:lang="en">Wachter S., Mittelstadt B., Floridi L. Why a right to explanation of automated decision-making does not exist in the general data protection regulation. SSRN Electronic Journal. 2016. http://doi.org/10.1093/idpl/ipx005.</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>
