Submissions are open for the upcoming issue.

Molecular Medicine Peer-reviewed scientific journal

Molecular Medicine

ОБЗОР2025, No. 3

Artificial intelligence-based information technologies in the era of personalized health assessment

Seliverstov P.V., Shapovalov V.V., Kravchuk Y.A., Salikova S.P., Shavaeva F.V., Isaeva P.A., Salmanova M.M., Arslanbekova R.M.

Authors and affiliations

P.V. Seliverstov(1), V.V. Shapovalov(2), Y.A. Kravchuk(1), S.P. Salikova(1),
F.V. Shavaeva(3), P.A. Isaeva(4), M.M. Salmanova(4), R.M. Arslanbekova(4)
1-Federal State Budgetary Military Educational Institution of Higher Education “Military Medical Academy named after S.M.Kirov”
of the Ministry of Defense of the Russian Federation, Lebedeva St., 6, Saint Petersburg, 194044, Russian Federation;
2-Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya str., 29, St. Petersburg, 194064, Russian Federation; 3-Kabardino-Balkarian State University named after H.M. Berbekov, Medical Academy, Chernyshevsky St., 173, Nalchik, 360004, Russian Federation; 4-Federal State Budgetary Educational Institution of Higher Education “Dagestan State Medical University”, Ministry of Health of the Russian Federation, Lenin Square, 1, Makhachkala, 367000, Russian Federation

Pages
11–18
Published
2025
Section
ОБЗОР
Молекулярная медицина, 2025, № 3Issue2025, No. 3Go to the issue →
How to cite

Abstract

Introduction. Personalized (precision) medicine is rapidly changing modern healthcare, shifting the focus from disease treatment to prevention and individualized patient approach. The integration of artificial intelligence (AI) technologies and molecular medicine opens new opportunities for early disease detection, risk factor (RF) assessment, and selection of optimal prevention and therapy considering genetic characteristics.
Objective. To analyze the role of AI-based information technologies in the context of personalized (molecular) medicine.
Material and methods. Scientific publications from the last 5 years were analyzed from PubMed and Scopus databases, demonstrating the effectiveness of AI algorithms in early diagnostics, successful examples of whole genome sequencing application, polygenic risk indices, and other genetic technologies for disease prediction.
Results. Modern AI-based information technologies in the context of personalized health assessment are considered: screening programs, intelligent analysis of medical data, genomic and other «omics» technologies. The prospects for implementing AI in clinical practice are discussed, including multimodal models combining clinical and molecular data, and current barriers (lack of resources, regulatory restrictions, ethical issues) to the implementation of personalized medicine are considered.
Conclusion. Digital molecular medicine using AI improves the effectiveness of disease prevention, diagnosis, and treatment, which is confirmed by both clinical and economic indicators, but requires a comprehensive approach to implementation and standardization.

Keywords

Authors and affiliations

P.V. Seliverstov(1), V.V. Shapovalov(2), Y.A. Kravchuk(1), S.P. Salikova(1),
F.V. Shavaeva(3), P.A. Isaeva(4), M.M. Salmanova(4), R.M. Arslanbekova(4)
1-Federal State Budgetary Military Educational Institution of Higher Education “Military Medical Academy named after S.M.Kirov”
of the Ministry of Defense of the Russian Federation, Lebedeva St., 6, Saint Petersburg, 194044, Russian Federation;
2-Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya str., 29, St. Petersburg, 194064, Russian Federation; 3-Kabardino-Balkarian State University named after H.M. Berbekov, Medical Academy, Chernyshevsky St., 173, Nalchik, 360004, Russian Federation; 4-Federal State Budgetary Educational Institution of Higher Education “Dagestan State Medical University”, Ministry of Health of the Russian Federation, Lenin Square, 1, Makhachkala, 367000, Russian Federation

References

Тайц Б.М. «10П медицина» в решении вопросов снижения смертности, увеличения продолжительности и повышения качества жизни пожилого населения. Клиническая геронтология. 2021; 27 (11–12): 76–9. https://doi.org/10.26347/1607-2499202111-12076-079 [Tayts BM. «P10 Medicine» for lower mortality, longer life expectancy and better quality of life in elderly people. Clin Gerontol. 2021; 27 (11–12): 76–9. https://doi.org/10.26347/1607-2499202111-12076-079 (in Russian)]

Blobel B., Kalra D. Editorial: Managing healthcare transformation towards P5 medicine. Front Med (Lausanne). 2023; 10:1244100. DOI: 10.3389/fmed.2023.1244100.

Denysyuk H.V., Pires I.M., Garcia N.M. A roadmap for empowering cardiovascular disease patients: a 5P-Medicine approach and technological integration. PeerJ. 2024; 12: e17895. DOI: 10.7717/peerj.17895.

Blobel B., Ruotsalainen P., Brochhausen M., Oemig F., Uribe G.A. Autonomous Systems and Artificial Intelligence in Healthcare Transformation to 5P Medicine – Ethical Challenges. Stud Health Technol Inform. 2020; 270: 1089–93. DOI: 10.3233/SHTI200330.

Селиверстов П.В., Бакаева С.Р., Шаповалов В.В. Оценка рисков социально значимых хронических неинфекционных заболеваний c использованием телемедицинской системы. Врач. 2020; 31 (10): 68–73. https://doi.org/10.29296/25877305-2020-10-13. [Seliverstov P, Bakaeva S, Shapovalov V. A telemedicine system in the assessment of risks for socially significant chronic non-communicable diseases. Vrach. 2020; 31 (10): 68–73. https://doi.org/10.29296/25877305-2020-10-13 (in Russian)]

Liu X., Faes L., Kale A.U., Wagner S.K., Fu D.J., Bruynseels A., Mahendiran T. еt al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019; 1 (6): 271–97. DOI: 10.1016/S2589-7500(19)30123-2.

Селиверстов П.В., Шаповалов В.В., Алешко О.В. Внедрение телемедицинских технологий на основе искусственного интеллекта в практику оказания амбулаторно-поликлинической помощи для проведения медицинского осмотра. Медицинский алфавит. 2023; 28: 44–9. https://doi.org/10.33667/2078-5631-2023-28-44-49. [Seliverstov P.V., Shapovalov V.V., Aleshko O.V. Introduction of telemedicine technologies based on artificial intelligence into practice of providing outpatient care for medical examination. Medical alphabet. 2023; 28: 44–9. https://doi.org/10.33667/2078-5631-2023-28-44-49 (in Russian)]

Lou S., Du F., Song W., Xia Y., Yue X., Yang D., Cui B., Liu Y., Han P. Artificial intelligence for colorectal neoplasia detection during colonoscopy: a systematic review and meta-analysis of randomized clinical trials. EClinicalMedicine. 2023; 66: 102341. DOI: 10.1016/j.eclinm.2023.102341.

Kim H., Park K.U. Clinical Circulating Tumor DNA Testing for Precision Oncology. Cancer Res Treat. 2023; 55 (2): 351–66. DOI: 10.4143/crt.2022.1026.

Sammut S.J., Crispin-Ortuzar M., Chin S.F. et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature. 2022; 601: 623–9. https://doi.org/10.1038/s41586-021-04278-5

Mars N., Koskela J.T., Ripatti P. et al. Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat Med. 2020; 26:549-557. https://doi.org/10.1038/s41591-020-0800-0

Селиверстов П.В. Перспективы использования телемедицинских технологий на основе искусственного интеллекта при проведении медицинского осмотра. Медицинский совет. 2024; 18 (5): 104–11. https://doi.org/10.21518/ms2024-072. [Seliverstov P.V. Prospects for the use of telemedicine technologies based on artificial intelligence during medical examination. Meditsinskiy Sovet. 2024; 18 (5): 104–11. https://doi.org/10.21518/ms2024-072 (in Russian)]

Johnson K.B., Wei W.Q., Weeraratne D., Frisse M.E., Misulis K., Rhee K., Zhao J., Snowdon J.L. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Transl Sci. 2021; 14 (1): 86–93. DOI: 10.1111/cts.12884.

Kwon J.M., Kim K.H., Jeon K.H., Park J. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography. 2019; 36 (2): 213–8. DOI: 10.1111/echo.14220.

Morozov S., Vladzymyrskyy A., Ledikhova N., Andreychenko A., Arzamasov K., Omelyanskaya O. et al. Diagnostic accuracy of artificial intelligence for analysis of 1.3 million medical imaging studies: the Moscow experiment on computer vision technologies [Preprint]. medRxiv. 2023. [cited 2025 May 14];2023.08.31.23294896. Available from: https://www.medrxiv.org/content/10.1101/2023.08.31.23294896v1.full

Perez M.V., Mahaffey K.W., Hedlin H., Rumsfeld J.S., Garcia A., Ferris T., Balasubramanian V. еt al. Apple Heart Study Investigators. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N. Engl. J. Med. 2019; 381 (20): 1909–17. DOI: 10.1056/NEJMoa1901183.

Короткова М.Н. Социальная политика государства: персонализированная медицина в контексте модернизации российской системы здравоохранения (теория и практика Пермского края). Общество: политика, экономика, право. 2020; 5. URL: https://cyberleninka.ru/article/n/sotsialnaya-politika-gosudarstva-personalizirovannaya-meditsina-v-kontekste-modernizatsii-rossiyskoy-sistemy-zdravoohraneniya. [Korotkova MN. State social policy: personalized medicine in the context of modernization of the Russian healthcare system (theory and practice of Perm Krai). Society: Politics, Economics, Law. 2020;(5). Available from: https://cyberleninka.ru/article/n/sotsialnaya-politika-gosudarstva-personalizirovannaya-meditsina-v-kontekste-modernizatsii-rossiyskoy-sistemy-zdravoohraneniya [cited 2025 May 14]. (in Russian)]

Селиверстов ПВ. Психология адаптации пациентов к использованию искусственного интеллекта при проведении скрининга хронических неинфекционных заболеваний. Медицинский Совет. 2024; 23: 266–72. https://doi.org/10.21518/ms2024-551. [Seliverstov PV. Psychology of patient adaptation to the use of artificial intelligence in screening for chronic noncommunicable diseases. Meditsinskiy sovet = Medical Council. 2024; 23: 266–72. https://doi.org/10.21518/ms2024-551 (in Russian)]

Lucas G.M., Rizzo A., Gratch J., Scherer S., Stratou G., Boberg J. et al. Reporting mental health symptoms: breaking down barriers to care with virtual human interviewers. Front Robot AI. 2017; 4: 51. DOI: 10.3389/frobt.2017.00051

Arndt B.G., Beasley J.W., Watkinson M.D., Temte J.L., Tuan W.J., Sinsky C.A., Gilchrist V.J. Tethered to the EHR: Primary Care Physician Workload Assessment Using EHR Event Log Data and Time-Motion Observations. Ann Fam Med. 2017; 15 (5): 419–26. DOI: 10.1370/afm.2121.

Министерство здравоохранения Российской Федерации. Паспорт федерального проекта «Создание единого цифрового контура в здравоохранении на основе единой государственной информационной системы в сфере здравоохранения (ЕГИСЗ)» [Интернет]. Москва: Минздрав России; [обновлено 2023; цитировано 2025 май 14]. Доступно по: https://minzdrav.gov.ru. [Ministry of Health of the Russian Federation. Passport of the federal project «Creation of a unified digital healthcare framework based on the Unified State Health Information System (USHIS)» [Internet]. Moscow: Ministry of Health of Russia; [updated 2023; cited 2025 May 14]. Available from: https://minzdrav.gov.ru (in Russian)]

Cowie M.R., Bax J., Bruining N., Cleland J.G., Koehler F., Malik M., Pinto F., van der Velde E., Vardas P. e-Health: a position statement of the European Society of Cardiology. Eur Heart J. 2016; 37 (1): 63–6. DOI: 10.1093/eurheartj/ehv416.

Sørensen K., Pelikan J.M., Röthlin F., Ganahl K., Slonska Z., Doyle G., Fullam J. еt al. HLS-EU Consortium. Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU). Eur. J. Public Health. 2015; 25 (6): 1053–8. DOI: 10.1093/eurpub/ckv043.

How to cite

Seliverstov P.V., Shapovalov V.V., Kravchuk Y.A., Salikova S.P., Shavaeva F.V., Isaeva P.A., Salmanova M.M., Arslanbekova R.M. (2025). Artificial intelligence-based information technologies in the era of personalized health assessment. Molecular Medicine, 23(3), 11–18. https://doi.org/10.29296/24999490-2025-03-02

Article search and quick navigation

Navigation