Practical implementation of artificial intelligence technologies during preventive medical examination

Introduction. Preventive medicine in Russia is a priority area, the key task of which is to combat the spread of chronic noncommunicable diseases (CKD). The latter are the cause of the premature death of hundreds of people. In this regard, the issues of developing and implementing early screening of NCDs, combined with advanced digital technologies based on artificial intelligence (AI), in clinical practice, are extremely relevant.

Aim. To develop a medical methodology for remote questionnaire screening (DAS) of CNID in young people.

Materials and methods. The study involved 3.155 people aged 19.6 ± 1.5 years (46.9% were men and 53.1% were women). Preventive medical examination of all participants was carried out using DAS.

Results. A high degree of NCD risk was found in 11.7%, an average in 30.9%, and a low in 57.4% of the subjects. The most frequent complaints were from the endocrine (28.9%), digestive (21.8%), respiratory (21.1%), cardiovascular (20.1%) and oncological alertness (8.1%). In 75.7% of cases, the presence of risk factors (RFS) was determined by two or more pathology profiles. Satisfaction with DAS use among the surveyed was 96.6%, and among medical workers 91.7%.

Conclusions. 1. The use of DAS FR HNIZ increases the compliance of patients to undergo a preventive medical examination.

2. The use of statistical methods confirms the effectiveness of the integrated assessment of health and the effectiveness of the detection of NID according to the main socially significant profiles of pathology.

3. The system identifies the most common NIDF, the degree of their severity, and also identifies people with critical FD who need priority care. This option allows you to optimize patient routing, reducing the one-time burden on the medical institution as a whole and on a specific specialist.

4. Depending on the identified NHS disorders and their severity, a set of recommendations has been developed, which implements a personalized approach.

5. The use of DAS FR HNIZ in young people has shown significant social and economic effectiveness.

1. Drapkina OM, Kontsevaya AV, Kalinina AM, Avdeev SM, Agaltsov MV, Alexandrova LM et al. 2002 Prevention of chronic non-communicable diseases in the Russian Federation. National guidelines. Cardiovascular Therapy and Prevention (Russian Federation). 2022;21(4):32–35. (In Russ.) https://doi.org/10.15829/1728-8800-2022-3235.

2. Seliverstov PV, Grinevich VB, Shapovalov VV, Kryukov EV. Improving the effectiveness of screening for chronic noncommunicable diseases using artificial intelligence-based technologies. Lechaschi Vrach. 2024;27(4):97–104. (In Russ.) https://doi.org/10.51793/OS.2024.27.4.014.

3. Hu Y, Yang Y, Gao Y, Zhao L, Chen L, Sui W, Hu J. The impact of chronic diseases on the health-related quality of life of middle-aged and older adults: the role of physical activity and degree of digitization. BMC Public Health. 2024;24(1):2335. https://doi.org/10.1186/s12889-024-19833-8.

4. Peña-Silva RA, Reyes-González JS. Following the roadmap outlined by the World health Organization: Innovation for the control of chronic noncommunicable diseases. Biomedica. 2024;44(1):5–10. https://doi.org/10.7705/biomedica.7603.

5. Пальцев МА (ред.). Медицина будущего. Персонализированная медицина: опыт прошлого, реалии завтрашнего дня. М.: Российская академия наук; 2020. 152 с.

6. Sun N, Ogulur I, Mitamura Y, Yazici D, Pat Y, Bu X et al. The epithelial barrier theory and its associated diseases. Allergy. 2024;79(12):3192–3237. https://doi.org/10.1111/all.16318.

7. Chandra M. Developmental Origins of Non-Communicable Chronic Diseases: Role of Fetal Undernutrition and Gut Dysbiosis in Infancy. Children. 2024;11(11):1387. https://doi.org/10.3390/children11111387.

8. Kolmykova TS, Sadoyan DS, Gribov RV. Digital technologies in transformation of economic architecture: prospects and threats. Management Accounting. 2021;(8-2):266–272. (In Russ.) https://doi.org/10.25806/uu8-22021266-272.

9. Ezeamii VC, Okobi OE, Wambai-Sani H, Perera GS, Zaynieva S, Okonkwo CC et al. Revolutionizing Healthcare: How Telemedicine Is Improving Patient Outcomes and Expanding Access to Care. Cureus. 2024;16(7):e63881. https://doi.org/10.7759/cureus.63881.

10. Бойцов СА, Драпкина ОМ, Калинина АМ, Ипатов ПВ, Вергазова ЭК, Гамбарян МГ и др. Организация проведения диспансеризации определенных групп взрослого населения. М.; 2017. 199 с. Режим доступа: https://mpmo.ru/content/2018/01/Metodicheskie-rekomendatsii-Organizatsiya-provedeniya-dispanserizatsii-opredelennyh-grupp-vzroslogo-naseleniya-4-izdanie.pdf.

11. Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23(1):689. https://doi.org/10.1186/s12909-023-04698-z.

12. Shende V, Wagh V. Role of Telemedicine and Telehealth in Public Healthcare Sector: A Narrative Review. Cureus. 2024;16(9):e69102. https://doi.org/10.7759/cureus.69102.

13. Seliverstov PV, Shapovalov VV, Aleshko OV. Introduction of telemedicine technologies based on artificial intelligence into practice of providing outpatient care for medical examination. Medical Alphabet. 2023;(28):44–49. (In Russ.) https://doi.org/10.33667/2078-5631-2023-28-44-49.

14. Sevostyanova EV, Nikolaev YuA, Polyakov VYa. The problem of multimorbidity in a modern therapeutic clinic. Bulletenʹ Sibirskoj Mediciny. 2022;21(1):162–170. (In Russ.) https://doi.org/10.20538/1682-0363-2022-1-162-170.

15. Skryabin VYu. Logical fallacies in medical practice. Vestnik of Saint Petersburg University. Medicine. 2022;17(3):154–165. (In Russ.) https://doi.org/10.21638/spbu11.2022.301.

16. Аксенова ЕИ, Бессчетнова ОВ. Показатели доступности и качества медицинской помощи, обеспечивающие удовлетворенность населения медицинской помощью в различных странах мира. Экспертный обзор. М.: ГБУ «НИИОЗММ ДЗМ»; 2021. 40 с.

17. Seliverstov PV, Bezruchko DS, Vasin AV, Grinevich VB, Semenov KP, Aleshko OV, Shapovalov VV. Telemedicine remote multidisciplinary questionnaire screening as a tool for early detection of chronic noncommunicable diseases. Meditsinskiy Sovet. 2023;17(6):311–321. (In Russ.) https://doi.org/10.21518/ms2023-070.

18. Lamotkin AI, Korabelnikov DI, Lamotkin IA, Livshitz SA, Perevalova EG. Artificial intelligence in healthcare and medicine: the history of key events, its significance for doctors, the level of development in different countries. Farmakoekonomika. Modern Pharmacoeconomics and Pharmacoepidemiology. 2024;17(2):243–250. (In Russ.) https://doi.org/10.17749/2070-4909/farmakoekonomika.2024.254.

19. Seliverstov PV, Bakaeva SR, Shapovalov VV, Aleshko OV. Telemedical technologies: from theory to practice. Meditsinskiy Sovet. 2022;16(23):366–372. (In Russ.) https://doi.org/10.21518/2079-701X-2022-16-23-366-372.

20. Borodulina EA, Gribova VV, Vdoushkina ES. Artificial intelligence technologies in medicine. Problems of establishment. Vrach. 2023;34(3):5–8. (In Russ.) https://doi.org/10.29296/25877305-2023-03-01.

21. Makulin AV, Rubenko AS. Philosophy, logic, and visualization of clinical thinking. Society: Philosophy, History, Culture. 2021;82(2):13–20. (In Russ.) Available at: https://archive.dom-hors.ru/nauchniy-zhurnal-obschestvo-filosofiya-istoriya-kultura/2021/2.

22. Borisov IV. Blockchain platform as a tool for digitalization of management processes in health care. The Eurasian Scientific Journal. 2023;15(1):33ECVN123. (In Russ.) Available at: https://esj.today/PDF/33ECVN123.pdf.

23. Minasova EY. Logic of medical thinking. Bulletin of Medical Internet Conferences. 2018;8(11):592–596. (In Russ.) Available at: https://medconfer.com/files/archive/2018-11/2018-11-4-A-18330.pdf.

24. Berseneva EA, Umnov SV, Umnov MS, Agamov ZKh. Blockchain technology in healthcare digitalization. Profilakticheskaya Meditsina. 2023;26(4):95–99. (In Russ.) https://doi.org/10.17116/profmed20232604195.

25. Walters R, Leslie SJ, Polson R, Cusack T, Gorely T. Establishing the efficacy of interventions to improve health literacy and health behaviours: a systematic review. BMC Public Health. 2020;20(1):1040. https://doi.org/10.1186/s12889-020-08991-0.

26. Seliverstov PV, Shapovalov V, Vasin A, Grinevich V, Semenov К. Evaluation of the Effectiveness of an AI-Based Telemedicine System for Remote Screening of Chronic Disease Risks. JoWUA. 2025;16(1):217–229. https://doi.org/10.58346/JOWUA.2025.I1.013.

27. Vertkin AL, Sediakina YuV, Pogonin AV, Romanenko II. “Hippocrates” – support system for medical decision-making for primary care for early detection of socially significant diseases. Medical Alphabet. 2021;(7):11–14. (In Russ.) https://doi.org/10.33667/2078-5631-2021-7-11-14.

28. Seliverstov PV. Prospects for the use of telemedicine technologies based on artificial intelligence during medical examination. Meditsinskiy Sovet. 2024;18(5):104–111. (In Russ.) https://doi.org/10.21518/ms2024-072.