Medicine of the future: the role of artificial intelligence in optimizing nutrition for the health of the Russian population

• Viktor A. Tutelyan
• Inna Yu. Tarmaeva
• Murat A. Kade
• Dmitriy B. Nikityuk

Abstract

One of the most pressing medical, social and government tasks is to ensure health saving, improve the quality of life and increase the life expectancy of the Russian population. The most important lever to ensure the solution of these issues is the optimization of nutrition and the use of methods of artificial intelligence (AI) and new information technologies to support decision-making in this area.

The purpose of the study was to familiarize specialists with the Scientific Instrument for Nutrition Analysis (SINA), created on the basis of AI technologies to optimize nutrition and prevent alimentary diseases in order to preserve the health of the population of the Russian Federation.

Results. A patent was created and received for a scientific web tool (SINA) based on AI technologies for actual nutrition analyzing with subsequent assessment of health status and generation of recommendations, including a personalized diet, which is formed based on individual human needs for energy, nutrients and bioactive compounds, taking into account genome, gender characteristics, age, food preferences, profession, diseases, medication treatment, clinical blood parameters, individual morphological and constitutional characteristics, his physical activity, etc.

Conclusion. The results of using SINA can reduce the labor costs of doctors; personalize diets as much as possible taking into account many factors; increase patient adherence to the prescribed diet and recommendations; increase public awareness of nutrition issues, reduce morbidity rates and improve public health. The high-tech approach using AI is advanced and is capable of solving many key challenges in the field of health care, prevention and medicine in general, and improve the quality of life of the population.

Keywords: digital nutritionology; optimal nutrition; alimentary diseases; artificial intelligence; digitalization

References

1. Tutelyan V.A., Nikityuk D.B. International and Russian mechanisms for integrating innovations and experience to optimize the nutrition of the population. Voprosy pitaniia [Problems of Nutrition]. 2023; 92 (3): 5–14. DOI: https://doi.org/10.33029/0042-8833-2023-92-3-5-14 (in Russian)

2. Kuz’min S.V., Rusakov V.N., Setko A.G. Assessment of the state of actual nutrition in the population of the Russian Federation. Gigiena i sanitariya [Hygiene and Sanitation]. 2024; 103 (1): 58–66. DOI: https://doi.org/10.47470/0016-9900-2024-103-1-58-66 (in Russian)

3. Goldina T.A., Burmistrov V.A., Efimenko I.V., Khoroshevsky V.F. Artificial intelligence in healthcare: Real World Data and Patient Voice – are we ready for new realities? Meditsinskie tekhnologii. Otsenka i vybor [Medical Technologies. Evaluation and Selection]. 2021; 43 (2): 22‑31. DOI: https://doi.org/10.17116/medtech20214302122 (in Russian)

4. Tutelyan V.A., Nikityuk D.B. (eds). Nutritionology and clinical dietology: national guidelines. 2nd ed. Moscow: GEOTAR-Media, 2021: 1008 p. (Series «National Guidelines»). DOI: https://doi.org/10.33029/9704-6280-5-NKD-2021-1-1008 (in Russian)

5. Salagay O.О., Sakharova G.M., Antonov N.S., Nikitina S.Yu., Stadnik N.M., Starodubov V.I. Assessment of the prevalence of behavioral risk factors and their impact on the health of the adult population in the Russian Federation. Voprosy statistiki [Problems of Statistic]. 2023; 30 (2): 72–86. DOI: https://doi.org/10.34023/2313-6383-2023-30-2-72-86 (in Russian)

6. Tutelyan V.A., Nikityuk D.B., Tarmaeva I.Yu. Formation of the All-Russian Education System in the field of healthy food of the population. Gigiena i sanitariya [Hygiene and Sanitation]. 2023; 102 (10): 1012–8. DOI: https://doi.org/10.47470/0016-9900-2023-102-10-1012-1018 (in Russian)

7. Tutelyan V.A., Musina O.N., Balykhin M.G., Shchetinin M.P., Nikityuk D.B. Digital nutrition: the use of information technologies in the development and improvement of food products. Textbook. Moscow: MGUPP, 2020: 378 p. ISBN 978-5-93957-969-8 (in Russian)

8. Onishchenko G.G. Development of the risk analysis methodology given the current safety challenges for public health in the Russian Federation: vital issues and prospects. Analiz riska zdorov’yu [Health Risks Analysis]. 2023; (4): 4–18. DOI: https://doi.org/10.21668/health.risk/2023.4.01.eng (in Russian)

9. Gusev A.V., Morozov S.P., Kutichev V.A., Novitsky R.E. Legal regulation of artificial intelligence software in healthcare in the Russian Federation. Meditsinskie tekhnologii. Otsenka i vybor [Medical Technologies. Evaluation and Selection]. 2021; 43 (1): 36–45. DOI: https://doi.org/10.17116/medtech20214301136 (in Russian)

10. Orlov N.A., Kislitsyn A.A., Kambarov A.O., Baturin A.K., Nikityuk D.B., Tutelyan V.A. Digital nutrition: spectral portraits of optimal diet. Nauchnaya vizualizatsiya [Scientific Visualization]. 2020; 12 (2): 139–50. DOI: https://doi.org/10.26583/sv.12.2.11 (in Russian)

11. Ji Kang, Rosalam Che Me, Khairul Manami Kamarudin, Ruhaizin Sulaiman Healthy lifestyle as a way to manage health risks: components and factors. Analytical review. Analiz riska zdorov’yu [Health Risks Analysis]. 2023; (4): 158–171. DOI: https://doi.org/10.21668/health.risk/2023.4.15 (in Russian)

12. Martinchik A.N., Baturin A.K., Mikhaylov N.A., Keshabyants E.E., Kambarov A.O. Development and assessment of the reliability of the basic healthy eating index for the Russian population. Voprosy pitaniia [Problems of Nutrition]. 2019; 88 (6): 34–44. DOI: https://doi.org/10.24411/0042-8833-2019-10062 (in Russian)

13. Gavrikov M.B., Kislitsyn A.A., Orlov Yu.N. Mathematical models in problems of personalized digital nutrition. Tendentsii razvitiya nauki i obrazovaniya (Samara) [Trends in the Development of Science and Education (Samara)]. 2023; 100 (4): 24–32. DOI: https://doi.org/10.18411/trnio-08-2023-p4 (in Russian)

All articles in our journal are distributed under the CC BY-NC-ND 4.0 (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International)

© GEOTAR-Media Publishing Group. The use of textual and illustrative content from this publication for the training of any artificial intelligence systems - including machine learning models and neural networks - is strictly prohibited without the prior written consent of the copyright holder.