Artificial intelligence-based information technologies in the era of personalized health assessment
- Authors: Seliverstov P.V.1, Shapovalov V.V.2, Kravchuk Y.A.1, Salikova S.P.1, Shavaeva F.V.3, Isaeva P.A.4, Salmanova M.M.4, Arslanbekova R.M.4
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Affiliations:
- 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
- Peter the Great St. Petersburg Polytechnic University
- Kabardino-Balkarian State University named after H.M. Berbekov
- Federal State Budgetary Educational Institution of Higher Education “Dagestan State Medical University”, Ministry of Health of the Russian Federation
- Issue: Vol 23, No 3 (2025)
- Pages: 11-18
- Section: Reviews
- URL: https://journals.eco-vector.com/1728-2918/article/view/689008
- DOI: https://doi.org/10.29296/24999490-2025-03-02
- ID: 689008
Cite item
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.
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About the authors
Pavel V. Seliverstov
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
Author for correspondence.
Email: seliverstovpv@yandex.ru
ORCID iD: 0000-0001-5623-4226
SPIN-code: 6166-7005
Associate Professor of the 2nd Department (advanced medical therapy), Candidate of Medical Sciences, Associate Professor
Russian Federation, Lebedeva St., 6, Saint Petersburg, 194044Valentin V. Shapovalov
Peter the Great St. Petersburg Polytechnic University
Email: valshapovalov@mail.ru
ORCID iD: 0000-0002-9764-4018
SPIN-code: 7996-2771
Professor of the Institute of Biomedical Systems and Biotechnology, Doctor of Technical Sciences, Professor
Russian Federation, Politechnicheskaya str., 29, St. Petersburg, 194064Yurii A. Kravchuk
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
Email: kravchuk2003@mail.ru
ORCID iD: 0000-0001-8347-0531
SPIN-code: 6767-5189
Professor of the 2nd Department (Therapy for Advanced Medical Studies), Doctor of Medical Sciences, Professor
Russian Federation, Lebedeva St., 6, Saint Petersburg, 194044Svetlana P. Salikova
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
Email: salikova.1966@bk.ru
ORCID iD: 0000-0003-4839-9578
SPIN-code: 2012-8481
Professor of the 2nd Department (Therapy for Advanced Medical Studies), Doctor of Medical Sciences, Professor
Russian Federation, Lebedeva St., 6, Saint Petersburg, 194044Fatima V. Shavaeva
Kabardino-Balkarian State University named after H.M. Berbekov
Email: shavaevafv@mail.ru
ORCID iD: 0000-0002-1767-9975
Associate Professor of the Medical Academy, Candidate of Biological Sciences
Russian Federation, Chernyshevsky St., 173, Nalchik, 360004Patimat A. Isaeva
Federal State Budgetary Educational Institution of Higher Education “Dagestan State Medical University”, Ministry of Health of the Russian Federation
Email: isaeva_80@inbox.ru
ORCID iD: 0009-0005-5140-320X
Postgraduate Student
Russian Federation, Lenin Square, 1, Makhachkala, 367000Madina M. Salmanova
Federal State Budgetary Educational Institution of Higher Education “Dagestan State Medical University”, Ministry of Health of the Russian Federation
Email: 1258madina1258@gmail.com
ORCID iD: 0009-0006-0149-6644
6th year Student
Russian Federation, Lenin Square, 1, Makhachkala, 367000Rukiyat M. Arslanbekova
Federal State Budgetary Educational Institution of Higher Education “Dagestan State Medical University”, Ministry of Health of the Russian Federation
Email: dggfddvhfdghv@gmail.com
ORCID iD: 0009-0000-6076-0106
6th year student
Russian Federation, Lenin Square, 1, Makhachkala, 367000References
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