Abstract
Introduction. The introduction of artificial intelligence (AI) technologies in healthcare is one of the priority
strategic areas of the industry’s development. The tasks of understanding the social consequences of technological innovations are relevant. The paper presents an analysis of empirical data on the expectations of the Russian
population in connection with the prospects for the widespread use of AI technologies in healthcare.
Materials and methods. Data come from a representative sample of the population aged 18 and older conducted in Moscow and the Moscow region in 2024. The research used system of empirical indicators to characterize various aspects of attitudes toward the use of AI in healthcare in the context of social assessments and
behavioral orientations of respondents. Descriptive statistics methods and a logistic regression model applied in
a course of data analysis.
Results. It was found that positive expectations from the use of AI in healthcare are statistically more pronounced compared to negative expectations reflecting the possible risks of using AI. Among the positive expectations, respondents most often noted an increase in treatment effectiveness (61%); to a lesser extent – a decrease
in treatment costs, an expansion of treatment options for patients, and an efficient use of medical institution
resources (56–49%). Negative assessments reflecting the risks that may be associated with the introduction of AI
in healthcare – a decrease in the qualifications of doctors due to excessive reliance on technology in their work;
privacy issues; data manipulation and bias in recommendations; lack of understanding of how AI technologies
form recommendations – received 51–44%. Differences in the evaluative focus of expectations are significantly
associated with age and education, as well as the level of satisfaction with medical care. Binary logistic regression
model help to identify variables influencing trust in healthcare with extensive use of AI technologies.
Discussion. The research identified mass consciousness mixed expectations regarding the introduction of
AI technologies in healthcare. For successful adaptation to innovation, it is advisable to take into account the
problematic aspects of expectations the study considered. It is important to ensure the human dimension of technological and organizational-managerial decisions in the field of medical care
References
Asan, O., Bayrak, F.M., Choudhury, A. (2020). Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. Journal of Medical Internet Research, 22(6), p. e15154. DOI 10.2196/15154. EDN KVOSDV.
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quaterly, 13 (3), p. 319-340. DOI 10.2307/249008.
Venkatesh, V., Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46 (2), pp. 186-204. DOI 10.1287/mnsc.46.2.186.11926. EDN FNVBJN.
Damerji, H., Salimi, D. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, 30 (2), pp. 107-130. DOI 10.1080/09639284.2021.1872035. EDN DWMKUI.
Miltgen, L., Popovic, C., Oliveira, T. (2013). Determinants of end-user acceptance of biometrics: Integrating the "Big 3" of technology acceptance with privacy context. Decision Support Systems, 56, pp. 103-114. DOI 10.1016/j.dss.2013.05.010.
Choung, H., David, P., Ross, A. (2022). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human-Computer Interaction, 39 (9), pp. 1727–1739. DOI 10.1080/10447318.2022.2050543. EDN CUNYAG.
Zhang, M., Luo, М., Nie, R., Zhang, Y. (2017). Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology. International Journal of Medical Informatics, 108, pp. 97-109. DOI 10.1016/j.ijmedinf.2017.09.016.
Gansser, O.A. Reich, C.S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 101535. DOI 10.1016/j.techsoc.2021.101535. EDN PDWHAO.
Ghazizadeh, M., Lee, J.D., Boyle, L.N. (2012). Extending the technology acceptance model to assess automation. Cognition, Technology & Work, 14(1), pp. 39-49. DOI 10.1007/s10111-011-0194-3. EDN RSYPNQ.
Koenig, P.D. (2024) Attitudes toward artificial intelligence: combining three theoretical perspectives on technology acceptance. AI & Society. Journal of Knowledge, Culture and Communication, Published 08 June. DOI 10.1007/s00146-024-01987-z. EDN HSNOAJ.
Kitchin, R. (2017) Thinking critically about and researching algorithms. Information, Communication & Society, 20 (1), pp. 14-29. DOI 10.1080/1369118X.2016.1154087.
Araujo, T., Helberger, N., Kruikemeier, S., de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), pp. 611-623. DOI 10.1007/s00146-019-00931-w. EDN EEIWSR.
Алмазов А.А., Бирюкова А.И., Власов В.В., Потапчик Е.Г., Сажина С.В., Шейман И.М., Шишкин С.В. Российское здравоохранение: перспективы развития: Доклад НИУ ВШЭ. Москва: Нац. исслед. ун-т "Высшая школа экономики", 2024. 60 с. ISBN 978-5-7598-2986-7. EDN WBQEHW.
Васильев Ю.А., Тыров И.А., Владзимирский А.В., Арзамасов К.М., Пестренин Л.Д., Шулькин И.М. Новая модель организации массовых профилактических исследований, основанная на автономном искусственном интеллекте для сортировки результатов флюорографии // Здоровье населения и среда обитания. 2023. Т. 31. № 11. С. 23-32. DOI 10.35627/2219-5238/2023-31-11-23-32. EDN SYIQBX.
Купатенко Я.Г., Мирук А.К., Ломоносова А.В., Козлова А.А. Искусственный интеллект в медицине: обзор текущей ситуации и тенденции // Cifra. Медико-биологические науки. 2024. № 2 (2). С.1-13. DOI 10.60797/BMED.2024.2.4. EDN KICZWM.
Крылов А.П. Использование искусственного интеллекта для анализа биомаркеров: новые горизонты персонализированной медицины // Терапевт. 2024. №7. С. 18-24. DOI 10.33920/MED-12-2407-02. EDN EAHHTV.
Шамшурин В.И., Шамшурина Н.Г. Социология врачебной помощи в цифровую эпоху // Вестник Томского государственного университета. Философия. Социология. Политология. 2020. № 53. С. 178-187. Х/53/19. DOI 10.17223/1998863. EDN ITHRNM.
Кочетова Ю.Ю. Этические риски искусственного интеллекта и перспективы совместного принятия решений в медицине // Человек. 2024.Т. 35. № 3. С. 96-106. DOI 10.31857/S0236200724030065. EDN HIBCRF.
Углева А.В., Шилова В.А., Карпова Е.А. Индекс "этичности" систем искусственного интеллекта в медицине: от теории к практике // Этическая мысль. 2024. Т. 24. № 1. С. 144-159. DOI 10.21146/2074-4870-2024-24-1-144-159. EDN DUYXGQ.
Orlova et al. (2023) Opinion research among Russian Physicians on the application of technologies using artifcial intelligence in the feld of medicine and health care. BMC Health Services Research, 23, p. 749. DOI 10.1186/s12913-023-09493-6. EDN FKXJAM.
Wittal, C., Hammer, D., Klein,F., Rittchen, J. (2023). Perception and Knowledge of Artificial Intelligence in Healthcare, Therapy and Diagnostics: A Population-Representative Survey. Journal of Biotechnology and Biomedicine, 6, pp. 129-139. DOI 10.26502/jbb.2642-91280077. EDN NKGCCP
Mantello, P.A., Ghotbi, N., Ho, M.T., et al. (2024) Gauging public opinion of AI and emotionalized AI in healthcare: findings from a nationwide survey in Japan. AI & Soc. DOI 10.1007/s00146-024-02126-4. EDN UJQTZG.
Liehner, G.L., Biermann, H., Hick, A., Brauner, Ph., Zieflе, M. (2023). Perceptions, Attitudes and Trust Towards Artificial Intelligence - An Assessment of the Public Opinion. Artificial Intelligence and Social Computing, 72, pp. 32-41. DOI 10.54941/ahfe1003271.
Gillespie, N., Lockey, S., Curtis, C., Pool, J., & Akbari, A. (2023). Trust in Artificial Intelligence: A Global Study. The University of Queensland and KPMG Australia, 82 p. DOI 10.14264/00d3c94.
Социологический энциклопедический словарь. На русском, английском, немецком, французском и чешском языках. Редактор-координатор - академик РАН Г, В. Осипов. М. : Издательская группа ИНФРА М - НОРМА, 1998. 488 с. ISBN 5-89123-162-X. EDN TRWDJG.
Назаров М. М. Искусственный интеллект и алгоритмические решения в социальной сфере: представления молодёжи // Социологическая наука и социальная практика. 2023. Т. 11. № 3. С. 141-158. DOI 10.19181/snsp.2023.11.3.7. EDN PSIJNC.
Иванов В.Н., Насриддинов Т.Г., Мчедлова Е.М., Харченко В.С., Назаров М.М. Россияне о перспективах / Россия: центр и регионы. Сборник научных статей. Сборник научных статей. 2023. Выпуск 29. Архангельск, Северный (Арктический) федеральный университет имени М.В. Ломоносова. С. 4-117. ISBN 978-5-261-01661-8. EDN UXXLSP.
Asan, O., Bayrak, F.M., Choudhury, A. (2020). Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. Journal of Medical Internet Research, 22(6), p. e15154. https://doi.org/10.2196/15154. https://elibrary.ru/kvosdv.
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quaterly, 13(3), pp. 319-340. https://doi.org/10.2307/249008.
Venkatesh, V., Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), p. 186. https://doi.org/10.1287/mnsc.46.2.186.11926. https://elibrary.ru/fnvbjn.
Damerji, H., Salimi, D. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, 30 (2), pp. 107-130.
https://doi.org/10.1080/09639284.2021.1872035. https://elibrary.ru/dwmkui.
Miltgen, L., Popovic, C., Oliveira, T. (2013). Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context. Decision Support Systems, 56, pp. 103–114. https://doi.org/10.1016/j.dss.2013.05.010.
Choung, H., David, P., Ross, A. (2022). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39 (9), pp. 1727–1739. https://doi.org/10.1080/10447318.2022.2050543. https://elibrary.ru/cunyag.
Zhang, M., Luo, М., Nie, R., Zhang, Y. (2017). Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology. International Journal of Medical Informatics, 108, pp. 97–109. https://doi.org/10.1016/j.ijmedinf.2017.09.016.
Gansser, O.A. Reich, C.S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, p. 101535.
https://doi.org/10.1016/j.techsoc.2021.101535. https://elibrary.ru/pdwhao.
Ghazizadeh, M., Lee, J.D., Boyle, L.N. (2012). Extending the technology acceptance model to assess automation. Cognition, Technology & Work, 14(1), pp. 39–49. https://doi.org/10.1007/s10111-011-0194-3. https://elibrary.ru/rsypnq.
Koenig, P.D. (2024) Attitudes toward artificial intelligence: combining three theoretical perspectives on technology acceptance. AI & Society. https://doi.org/10.1007/s00146-024-01987-z. https://elibrary.ru/hsnoaj.
Kitchin, R. (2017) Thinking critically about and researching algorithms. Information, Communication & Society, 20 (1), pp. 14-29. https://doi.org/10.1080/1369118X.2016.1154087.
Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), pp. 611-623. https://doi.org/10.1007/s00146-019-00931-w. https://elibrary.ru/eeiwsr.
Almazov, A.A., Biryukova, A.I., Vlasov, V.V., Potapchik, E.G., Sazhina, S.V., Sheiman, I.M., Shishkin, S.V. (2024) Russian healthcare: Development Prospects: Report of the Higher School of Economics. Moscow, National Research University of Higher School of Economic. 60 p. ISBN 978-5-7598-2986-7. https://elibrary.ru/wbqehw.
Vasilev, Yu.A., Tyrov, I.A., Vladzymyrskyy, A.V., Arzamasov, K.M., Pestrenin, L.D., Shulkin, I.M. (2023) A new model of organizing mass screening based on stand-alone artificial intelligence used for fluorography image triage. Public Health and Life Environment – PH&LE, 31(11), pp. 23–32. https://doi.org/10.35627/2219-5238/2023-31-11-23-32. https://elibrary.ru/syiqbx.
Kupatenko, Y.G., Miruk, A.K., Lomonosova, A.V., Kozlova, A.A. (2024) Artificial intelligence in medicine: an overview of the current situation and tendencies. Cifra. Biomedical sciences, 2 (2), p. 4. https://doi.org/10.60797/BMED.2024.2.4. https://elibrary.ru/kiczwm.
Krylov, A.P. (2024) AI in biomarkers analysis: new horizons for personalized medicine. Terapevt, 7, pp. 18-24. https://doi.org/10.33920/MED-12-2407-02. https://elibrary.ru/eahhtv.
Shamshurin, V.I., Shamshurina, N.G. (2020). The sociology of medical care in the digital age. Vestnik Tomskogo gosudarstvennogo universiteta. Filosofiya. Sotsiologiya. Politologiya – Tomsk State University Journal of Philosophy, Sociology and Political Science, 53, pp. 178-187. https://doi.org/10.17223/1998863Х/53/19. https://elibrary.ru/ithrnm.
Kochetova, Yu.Y. (2024). Ethical risks of artificial intelligence and prospects for joint decision-making in medicine. The human being, 35 (3), pp. 96-106. https://doi.org/10.31857/S0236200724030065. https://elibrary.ru/hibcrf.
Ugleva, A.V., Shilova, V.A., Karpova, E.A. (2024). The index of "ethics" of artificial intelligence systems in medicine: from theory to practice. Ethical Thought, 24 (1), pp. 144-159. https://doi.org/10.21146/2074-4870-2024-24-1-144-159. https://elibrary.ru/duyxgq.
Orlova et al. (2023) Opinion research among Russian Physicians on the application of technologies using artifcial intelligence in the field of medicine and health care. BMC Health Services Research, 23, p. 749. https://doi.org/10.1186/s12913-023-09493-6. https://elibrary.ru/fkxjam.
Wittal, C., Hammer, D., Klein,F., Rittchen, J. (2023). Perception and Knowledge of Artificial Intelligence in Healthcare, Therapy and Diagnostics: A Population-Representative Survey. Journal of Biotechnology and Biomedicine, 6, pp. 129-139. https://doi.org/10.26502/jbb.2642-91280077. https://elibrary.ru/nkgccp.
Mantello, P.A., Ghotbi, N., Ho, M.T., et al. (2024) Gauging public opinion of AI and emotionalized AI in healthcare: findings from a nationwide survey in Japan. AI & Society. https://doi.org/10.1007/s00146-024-02126-4. https://elibrary.ru/ujqtzg.
Liehner, G.L., Biermann, H., Hick, A., Brauner, Ph., Zieflе, M. (2023). Perceptions, Attitudes and Trust Towards Artificial Intelligence - An Assessment of the Public Opinion. Artificial Intelligence and Social Computing, 72, pp. 32–41. https://doi.org/10.54941/ahfe1003271.
Gillespie, N., Lockey, S., Curtis, C., Pool, J., Akbari, A. (2023). Trust in Artificial Intelligence: A Global Study. The University of Queensland and KPMG Australia, 82 p. https://doi.org/10.14264/00d3c94.
The Sociological Encyclopedic Dictionary (1998). Available in Russian, English, German, French and Czech. The coordinating editor is Academician of the Russian Academy of Sciences G. V. Osipov. Moscow, INFRA M - NORMA Publishing Group, 488 p. ISBN 5-89123-162-Х. https://elibrary.ru/trwdjg.
Nazarov, M.M. (2023) Artificial Intelligence and algorithmic solutions in the social sphere: youth attitudes. Sociologicheskaja nauka i social’naja praktika, 11 (3), pp. 141-158. https://doi.org/10.19181/snsp.2023.11.3.7. https://elibrary.ru/psijnc.
Ivanov, V.N., Nasriddinov, T.G., Mchedlova, E.M., Kharchenko, V.S., Nazarov, M.M. (2023) Russians about prospects. Russia: Сenter and Regions. Collection of scientific articles. Arkhangelsk, Northern (Arctic) Federal University named after M.V. Lomonosov, 29, pp. 4-117. ISBN 978-5-261-01661-8. https://elibrary.ru/uxxlsp.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2025 Management Issues