The possibilities of using artificial intelligence technologies in military epidemiology
https://doi.org/10.51922/2074-5044.2025.3.77
Abstract
The article presents the prospects of using artificial intelligence technologies in the field of military epidemiology. Artificial intelligence is being actively integrated into military healthcare. The use of accounting, analysis, and decision support systems with artificial intelligence technologies in the field of military epidemiology is possible when monitoring the health of military personnel, assessing the epidemiological situation, conducting sanitary and epidemiological reconnaissance, predicting outbreaks and epidemics of infectious diseases, monitoring biological threats, and optimizing medical logistics. AI technologies in military epidemiology can be used to automate the accounting and registration of morbidity, retrospective and operational epidemiological analysis, establish a mechanism for the development of the epidemic process of infectious diseases, modeling the situation and monitor possible complications of sanitary and epidemiological arrest in order to increase the effectiveness of timely measures to prevent the occurrence and spread of infectious diseases in the Armed Forces.
About the Authors
I. V. FedorovaBelarus
V. A. Tumar
Belarus
References
1. Bakaev, V. V. Primenenie iskusstvennogo intellekta dlya ukrepleniya i razvitiya sistemy epidemiologicheskogo kontrolya (obzor literatury) / V. V. Bakaev, T. Yu. Gashenko, S. S. Mardanly [i dr.] // Epidemiologiya i infekcionnye bolezni. – 2024. – T. 29, № 3. – S. 126–134.
2. Laboratorii informacionno-analiticheskih sistem i bioinformatiki OIPI NAN Belarusi. Medicinskie informacionnye sistemy, servisy i bioinformatika [Elektronnyj resurs]. – URL: http://ehealth.by. – Data dostupa: 26.04.2025.
3. Mamedova, L. E. Osnovnye aspekty tekhnologii iskusstvennogo intellekta / L. E. Mamedova, L. N. Ivanova, E. S. Altaev // Izvestiya vysshih uchebnyh zavedenij. Seriya «Ekonomika, finansy i upravlenie proizvodstvom». – 2023. – T.57, № 3. – S. 78–88.
4. Federal’nyj zakon ot 24.04.2020 N 123-FZ «O provedenii eksperimenta po ustanovleniyu special’nogo regulirovaniya v celyah sozdaniya neobhodimyh uslovij dlya razrabotki i vnedreniya tekhnologij iskusstvennogo intellekta v sub”ekte Rossijskoj Federacii [Elektronnyj resurs]. – URL: https://www.consultant.ru/document/cons_doc_LAW_351127/ – Data dostupa: 18.03.2025.
5. Adib, B. R. Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges [Electronic resource] // International Journal of Intelligent Systems. – Mode of access: https://onlinelibrary.wiley.com/doi/full/10.1155/2023/8676366#reference. – Date of access: 23.04.2025.
6. Brockmann, K. BIO PLUS X: Arms Control and the Convergence of Biology and Emerging Technologies [Electronic resource] / Stockholm International Peace Research Institute, March 2019. – Mode of access: https://www.sipri.org/sites/default/files/2019-03/sipri2019_bioplusx_0.pdf – Access date: 23.04.2025.
7. Deadman, P. A. role for goal-oriented autonomous agents in modeling people-environment interactions in forest recreation / P. Deadman, R. H. Gimblett // Mathematical and Computer Modelling. – 1994. – Vol. 20, № 8. – P. 121–133.
8. Du, M. DeepLog: anomaly detection and diagnosis from system logs through deep learning [Electronic resource] / Conference on Computer and Communications Security, New York, November 2017. – Mode of access: https://www.tandfonline.com/doi/full/10.1080/08839514.2024.2440692#abstract. – Date of access: 26.04.2025.
9. Hussain, S. A. survey on conversational agents/ chatbots classifcation and design techniques / S. Hussain, O. A. Sianaki, N. Ababneh // Advances in Intelligent Systems and Computing. – 2019. – Vol. 927. – P. 946–956.
10. Lentzos, F. How to protect the world from ultra-targeted biological weapons [Electronic resource] / Bulletin of the Atomic Scientists, 7 December 2020. – Mode of access: https://thebulletin.org/premium/202012/how-to-protect-the-world-fromultra-targeted-biological-weapons/ – Access date: 25.04.2025.
11. Pataranutaporn P. AI-generated characters for supporting personalized learning and well-being / P. Pataranutaporn, V. Danry, J. Leong [et al.] // Nature Machine Intelligence. – 2021. – Vol. 3, № 12. – P. 1013–1022.
12. Shamir, L. Pattern recognition software and techniques for biological image analysis / L. Shamir, J. D. Delaney, N. Orlov [et al.] // PLoS Computational Biology. – 2010. – Vol. 6, № 11. – P. 1–10.
13. Shmueli, G. Predictive analytics in information systems research / G. Shmueli, O. R. Koppius // SSRN Electronic Journal. – 2011. – Vol. 35, № 3. – P. 533–572.
14. T. Zhang, T. Current trends in the development of intelligent unmanned autonomous systems / Zhang, Q. Li, C. Zhang [et al.] // Frontiers of Information Technology & Electronic Engineering. – 2017. – Vol. 18, № 1. – P. 65–85.
15. Zhaohui, Su. Threats with Artificial Intelligence and 6G Technologies: Literature Review and Critical Insights [Electronic resource] // J Med. Internet. Res., 2021. – Mode of access: https://pmc.ncbi.nlm.nih.gov/articles/PMC8153034. – Date of access: 26.04.2025.
Review
For citations:
Fedorova I.V., Tumar V.A. The possibilities of using artificial intelligence technologies in military epidemiology. Military Medicine. 2025;(3):77-84. (In Russ.) https://doi.org/10.51922/2074-5044.2025.3.77
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