NEURAL NETWORKS AND CHATBOTS: PERSPECTIVES OF THEIR APPLICATION IN PHYSICAL THERAPY IN UKRAINE
DOI:
https://doi.org/10.11603/1811-2471.2025.v.i2.15323Keywords:
military, neural networks, chatbots, physical therapy, artificial intelligenceAbstract
SUMMARY. The aim – to analyze the possibilities of implementing artificial intelligence, neural networks and chatbots in physical therapy and how they will contribute to improving the results of treatment and physical rehabilitation of military personnel and the civilian segment of the population of Ukraine in today's conditions.
Material and methods. The study used the bibliosemantic method and methods of content and structural-logical analysis.
Results. The implementation and promotion of artificial intelligence (AI) technologies in the field of medicine today is one of the main trends in the healthcare system that are changing modern world medicine. AI is actively used in the development of new drugs, improving the quality of medical diagnostics, treatment and physical rehabilitation of victims, to improve the quality of patient care and medical services in general. AI is able to significantly minimize costs in the healthcare sector. In the foreseeable future, the possibilities of artificial intelligence are practically limitless.
Conclusions. 1. As a result of diseases and injuries of various genesis and localization, victims experience changes in their physical and mental state of varying severity, morphological and functional disorders of various nature are observed, specific personal disharmony increases, and adaptive mechanisms of the body are disrupted. Due to the formation of combined pathology in victims, the need for improving comprehensive care and implementing new methods of treatment and physical rehabilitation is increasing. Currently, the creation of comprehensive highly effective programs for physical, medical, and psychological rehabilitation of victims using innovative technologies is relevant.
- The multi-vector nature of tasks requires the effective implementation of the latest rehabilitation methods in the process of recovering victims, including neural networks and chatbots, which have proven their ability to reduce recovery times with a higher-quality prolonged effect when used in physical rehabilitation. The world experience of using these methods deserves wider implementation in the clinical practice of our medical institutions as one of the tools for restoring the health of military and civilian personnel injured as a result of military operations in Ukraine.
References
Popadyukha YuA. Suchasni kompleksy, systemy ta prystroyi reabilitatsiynykh tekhnolohiy: navch. posib [Modern complexes, systems, and devices of rehabilitation technologies: Educational manual]. Kyiv: Center for Educational Literature; 2018. Ukrainian.
Popadyukha YuA. Suchasni komp'yuteryzovani kompleksy ta systemy u tekhnolohiyakh fizychnoyi reabilitatsiyi: navch. posib. [Modern computerized complexes and systems in physical rehabilitation technologies: Educational manual]. Kyiv: Center for Educational Literature; 2018. Ukrainian.
Vykhlyayev YuM. Reabilitatsiyni tekhnolohiyi i tekhnichni zasoby dlya vidnovlennya dlya lyudey z obmezhenymy fizychnymy mozhlyvostyamy: navch. posib. [Rehabilitation technologies and technical means for recovery for people with limited physical abilities: Educational manual]. National Technical University of Ukraine "Kyiv Polytechnic Institute". Vinnytsia: Rohalska I.O; 2012. Ukrainian.
Kukhtyk TV. Tekhnichni zasoby u fizychniy reabilitatsiyi: Opornyy navchal'no-metodychnyy interaktyvnyy kompleks [Technical means in physical rehabilitation: Reference educational-methodical interactive complex]. Kramatorsk: DITM MNTU named after Yu. Bugai; 2010. Ukrainian.
Amisha, Paras Malik, Monika Pathania, Vyas Kumar Rathaur. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019;8(7):2328-2331. DOI: 10. 4103/jfmpc.jfmpc_440_19. DOI: https://doi.org/10.4103/jfmpc.jfmpc_440_19
McCall HC, Richardson CG, Helgadottir FD, Chen FS. Evaluating a web-based social anxiety intervention: A randomized controlled trial among university students? J Med Internet Res. 2018;20:e91 DOI: 10.2196/jmir. 8630 DOI: https://doi.org/10.2196/jmir.8630
Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke. 2017;48:1416-9. DOI: https://doi.org/10.1161/STROKEAHA.116.016281
Anahtar MN, Yang JH, Kanjilal S. Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research. J. Clin. Microbiol. 2021;59(7):e0126020. DOI: 10.1128/JCM.01260-20. DOI: https://doi.org/10.1128/JCM.01260-20
Benjamins JW, Hendriks T, Knuuti J et al. A primer in artificial intelligence in cardiovascular medicine. Neth. Heart. J. 2019;27(9):392–402. DOI: 10.1007/s12471-019-1286-6. DOI: https://doi.org/10.1007/s12471-019-1286-6
Kamphuis B. Universiteiten kunnen belangstelling voor kunstmatige intelligentie niet aan. Ned. Omroep. Sticht. 2018. URL: nos.nl/artikel/2241732-universiteiten-kunnen-belangstelling-voor-kunstmatige-intelligentie-niet-aan.html.
Mervis J. MIT to use $350 million gift to bolster computer sciences. Science. 2018. URL: http://www.science.org/content/article/mit-use-350-million-gift-bolster-computer-sciences. DOI: https://doi.org/10.1126/science.aav7395
Nikkei Staff Writers. Japan plans 10 "AI hospitals" to ease doctor shortages. Tokyo: Nikkei Asia; 2018. Available from: https://asia.nikkei.com/Politics/Japan-plans-10-AI-hospitals-to-ease-doctor-shortages
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–243. DOI:10.1136/svn-2017-000101. DOI: https://doi.org/10.1136/svn-2017-000101
Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull. 2021;139(1):4–15. DOI:10.1093/bmb/ldab016. DOI: https://doi.org/10.1093/bmb/ldab016
Balyen L, Peto T. Promising artificial intelligence–machine learning–deep learning algorithms in ophthalmology. Asia Pac J Ophthalmol (Phila). 2019;8(3):264–272. DOI:10.22608/APO.2018479. DOI: https://doi.org/10.22608/APO.2018479
Juarez-Orozco LE, Knol RJJ, Sanchez-Catasus CA, et al. Machine learning in the integration of simple variables for identifying patients with myocardial ischemia. J Nucl Cardiol. 2020;27(1):147–155. DOI: 10.1007/s12350-018-1304-x. DOI: https://doi.org/10.1007/s12350-018-1304-x
Chen J, See K. Artificial intelligence for COVID-19: rapid review. J Med Internet Res. 2020;22:e21476. DOI: 10.2196/21476. DOI: https://doi.org/10.2196/21476
Saeed U, Shah SY, Ahmad J, et al. Machine learning empowered COVID-19 patient monitoring using non-contact sensing: an extensive review. J Pharm Anal. 2022;12(2):193–204. DOI: 10.1016/j.jpha.2021.12.006. DOI: https://doi.org/10.1016/j.jpha.2021.12.006
Ostaschenko TM, Kozak ND, Kozak DO. Coordination aspects of pharmacovigilance system adjustment in terms of the global COVID-19 pandemic. Ukr J Mil Med. 2021;2(4):161–165. DOI: 10.46847/ujmm.2021.4(2)-161. DOI: https://doi.org/10.46847/ujmm.2021.4(2)-161
Haymond S, McCudden C. Rise of the machines: artificial intelligence and the clinical laboratory. J Appl Lab Med. 2021;6(6):1640–1654. DOI: 10.1093/jalm/jfab075. DOI: https://doi.org/10.1093/jalm/jfab075
Paranjape K, Schinkel M, Hammer RD, et al. The value of artificial intelligence in laboratory medicine. Am J Clin Pathol. 2021;155(6):823–831. DOI: 10.1093/ajcp/aqaa170. DOI: https://doi.org/10.1093/ajcp/aqaa170
Dogan MV, Grumbach IM, Michaelson JJ, et al. Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study. PLoS One. 2018;13:e0190549. DOI:10.1371/journal.pone.0190549. DOI: https://doi.org/10.1371/journal.pone.0190549
Hui AT, Alvandi LM, Eleswarapu AS, et al. Artificial intelligence in modern orthopaedics: current and future applications. JBJS Rev. 2022;10(10). DOI: 10.2106/JBJS.RVW.22.00086. DOI: https://doi.org/10.2106/JBJS.RVW.22.00086
Federer SJ, Jones GG. Artificial intelligence in orthopaedics: A scoping review. PLoS One. 2021; 16(11):e0260471. DOI: 10.1371/journal.pone.0260471. DOI: https://doi.org/10.1371/journal.pone.0260471
Saygılı A, Albayrak S. An efficient and fast computer-aided method for fully automated diagnosis of meniscal tears from magnetic resonance images. Artif. Intell. Med., 2019;97:118–130. DOI: 10.1016/j.artmed.2018. 11.008. DOI: https://doi.org/10.1016/j.artmed.2018.11.008
Carballido-Gamio J, Yu A, Wang L et al. Hip fracture discrimination based on statistical multi-parametric modeling (SMPM). Ann. Biomed. Eng., 2019;47(11):2199–2212. DOI: 10.1007/s10439-019-02298-x. DOI: https://doi.org/10.1007/s10439-019-02298-x
