Nanorevolution in medicine: synergy of nanotechnology, artificial intelligence and digital innovation
- Authors: Seliverstov D.P.1
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Affiliations:
- “Federal State Budgetary Educational Institution of Higher Education “I.P. Pavlov First St. Petersburg State Medical University” of the Ministry of Healthcare of the Russian Federation
- Issue: Vol 26, No 7 (2024)
- Pages: 44-48
- Section: Innovations
- URL: https://journals.eco-vector.com/0025-8342/article/view/637227
- DOI: https://doi.org/10.29296/25879979-2024-07-06
- ID: 637227
Cite item
Abstract
This article analyses the potential of integrating nanotechnology, artificial intelligence and digital innovations in medicine. Promising applications of these technologies are discussed, including personalised nanomedicine, the fight against antibiotic resistance and the development of neuronanointerfaces. Particular attention is paid to the role of artificial intelligence in analysing data from nanosensors, designing nanostructures and controlling nanorobots. Ethical and legal aspects of the application of nanotechnology in medicine are discussed, including issues of data security and privacy.
Keywords
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About the authors
Daniil P. Seliverstov
“Federal State Budgetary Educational Institution of Higher Education “I.P. Pavlov First St. Petersburg State Medical University” of the Ministry of Healthcare of the Russian Federation
Author for correspondence.
Email: daniilseliverstov766@mail.ru
ORCID iD: 0009-0007-9828-9453
student of the Faculty of Dentistry
Russian Federation, 6-8, Lev Tolstoy St., St. Petersburg, 197022References
- Seliverstov P. V. Prospects for the use of telemedicine technologies based on artificial intelligence in medical examination. Medical Council. 2024; (5): 312–319. https://doi.org/10.21518/ms2024-072
- Tkachenko E.A., Pleshkov B.S., Raevskaya A.I. et al. Current epidemiological features of risk factors for acute cerebrovascular accident in people of different ages. Vrach. 2021; 32 (12): 63–68. https://doi.org/10.29296/25877305-2021-12-10
- Borodulina E.A., Gribova V.V., Vdoushkina E.S., Kiryushina T.M., Agarkova A.S. Artificial intelligence technologies in medicine. Problems of establishment. Vrach. 2023; (3): 5–8 https://doi.org/10.29296/25877305-2023-03-01
- Seliverstov P.V., Grinevich V.V.B., Shapovalov V.V. et al. Improving the efficiency of screening of chronic non-infectious diseases using artificial intelligence-based technologies. Lechachachy Vrach. 2024; 4 (27): 97–104. https://doi.org/10.51793/OS.2024.27.4.014
- Gavrilov D.V., Serova L.M., Korsakov I.N. et al. Cardiovascular diseases prediction by integrated risk factors assessment by means of machine learning. Vrach. 2020; 31 (5): 41–46. https://doi.org/10.29296/25877305-2020-05-08.
- What is deep learning? https://www.oracle.com/cis/artificial-intelligence/machine-learning/what-is-deep-learning/ (date of reference: 06.09.2024).
- Bayesian statistics in medical research. https://www.editverse.com/ru/bayesian-statistics-powering-medical-research-for-starters/#google_vignette (date of reference: 20.09.2024).
- Shelomentsev A.G., Bessonova T.N., Goncharova K.S., Modern models of population adaptation to dynamically changing socio-economic conditions of life. Vestnik ZabGU. 2020; (10). URL: https://cyberleninka.ru/article/n/sovremennye-modeli-adaptatsii-naseleniya-k-dinamichno-menyayuschimsya-sotsialno-ekonomicheskim-usloviyam-zhizni (date of reference: 26.09.2024).
- Nagarajan V.D., Lee S.L., Robertus J.L. et al. Artificial intelligence in the diagnosis and treatment of arrhythmias. Eur Heart J. 2021; 42 (38): 3904–3916. doi: 10.1093/eurheartj/ehab544
- Don E.S., Tarasov A.V., Epstein O.I. et al. Biomarkers in medicine: search, selection, study and validation. Clinical Laboratory Diagnostics. 2017; (1). URL: https://cyberleninka.ru/article/n/biomarkery-v-meditsine-poisk-vybor-izuchenie-i-validatsiya (date of reference: 16.09.2024).
- Romanchuk N.P., Bulgakova S.V., Volobuev A.N. et al. Alzheimer’s disease: biophysics, genetics, epigenetics, neuroimaging, bioelementology, nutritionology, treatment, prevention and neurotraining. Bulletin of Science and Practice. 2023; (2). URL: https://cyberleninka.ru/article/n/altsgeymera-bolezn-biofizika-genetika-epigenetika-neyrovizualizatsiya-bioelementologiya-nutritsiologiya-lechenie-profilaktika-i (date of reference: 01.09.2024).
- Tishkov D.S. Introduction of global learning and intercultural knowledge and competences in the practice of a dentist to increase oncological caution. ANI: pedagogy and psychology. 2020; 33 (4). URL: https://cyberleninka.ru/article/n/vnedrenie-globalnogo-obucheniya-i-mezhkulturnyh-znaniy-i-kompetentsiy-v-praktike-vracha-stomatologa-dlya-povysheniya (date of reference: 26.09.2024).
- Burkov V.D., Krapivin V.F., Soldatov V.Yu. et al. Nanotechnologies and problems of ecological monitoring. Vestnik MSUL - Lesnoy vestnik. 2011; (3). URL: https://cyberleninka.ru/article/n/nanotehnologii-i-problemy-ekologicheskogo-monitoringa (date of reference: 06.09.2024).
- Seliverstov P.V., Shapovalov V.V., Aleshko O.V. Introducing telemedicine technologies based on artificial intelligence in the practice of outpatient and polyclinic care for medical examination. Medical Alphabet. 2023; (28): 44–49. https://doi.org/10.33667/2078-5631-2023-28-44-49.
- Shishkova V.N., Adasheva T.V., Stakhovskaya L.V. The importance of metabolic markers in the development of a second ischemic stroke. Vrach. 2020; 31 (10): 65–68. https://doi.org/10.29296/25877305-2020-10-12.
- What is AI model training? https://engage-ai.co/ru/какое-обучение-модели-ИИ. (date of reference: 26.09.2024).
- Brusov O.S., Kuznetsova A.V., Senko O.V. Friendly artificial intellect in the betterment of public health. Vrach. 2020; (5): 80–84. https://doi.org/10.29296/25877305-2020-05-19.
- Gorokhov V. Social problems of nanotechnology. Higher education in Russia. 2008; (3). URL: https://cyberleninka.ru/article/n/sotsialnye-problemy-nanotehnologii (date of reference: 26.09.2024).
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