Optimizing Nutritional Outcomes: The Role of AI in Personalized Diet Planning

Sumit KR Sharma; Shweta Gaur

doi:10.36676/jrps.v15.i2.15

Authors

Sumit KR Sharma Defence Institute of Advanced Technology (DRDO), Pune https://orcid.org/0000-0001-6546-0348
Shweta Gaur Founder, Pueritia Foods pvt ltd https://orcid.org/0009-0009-6743-5707

DOI:

https://doi.org/10.36676/jrps.v15.i2.15

Keywords:

Personalized Nutrition, Artificial Intelligence, Machine Learning, Dietary Assessment, Nutritional Science, Health Technology

Abstract

The field of nutrition is undergoing a paradigm shift from generalized dietary guidelines to personalized nutrition, aiming to optimize health outcomes on an individual level. This paper explores the transformative role of artificial intelligence (AI) in facilitating personalized diet planning. Through the integration of AI technologies, including machine learning and data analytics, personalized diet plans can now be tailored to individual nutritional needs, preferences, and health goals with unprecedented precision. Case examples demonstrating the effective use of AI algorithms to improve dietary evaluation and modification are highlighted in this paper's thorough analysis of present AI applications in nutritional research. There are a number of obstacles to using AI in nutrition, despite the technology's promise. These include worries about data privacy and the need for strong, interpretable models. Future directions include the integration of emerging fields such as genomics and microbiomics, which could further refine AI-driven dietary recommendations. Ultimately, this paper demonstrates that while AI holds promising prospects for advancing personalized nutrition, it requires careful consideration of ethical, technological, and regulatory issues.

References

Castaneda, J., Calvet, L., Benito, S., Tondar, A., & Juan, A. A. (2023). Data science, analytics and artificial intelligence in e-health: Trends, applications and challenges. SORT, 47(1), 81–121. https://doi.org/10.57645/20.8080.02.2

Changhun Lee, Soohyeok Kim, Jayun Kim, Chiehyeon Lim, and Minyoung Jung (2022), Challenges of diet planning for children using artificial intelligence, Nutr Res Pract., 16(6), 801–812. doi: 10.4162/nrp.2022.16.6.801 DOI: https://doi.org/10.4162/nrp.2022.16.6.801

Christensen, L., Roager, H. M., Astrup, A., & Hjorth, M. F. (2018). Microbial enterotypes in personalized nutrition and obesity management. The American Journal of Clinical Nutrition, 108(4), 645–651. https://doi.org/10.1093/ajcn/nqy175 DOI: https://doi.org/10.1093/ajcn/nqy175

Dable-Tupas, G., Capirig, C. J., Roy, M., & Pathak, A. (2023). Nutrigenomics research: Methods and applications. In Role of Nutrigenomics in Modern-day Healthcare and Drug Discovery (pp. 35–82). Elsevier. https://doi.org/10.1016/B978-0-12-824412-8.00004-7 DOI: https://doi.org/10.1016/B978-0-12-824412-8.00004-7

Deanship of Quality and Academic Accreditation, Department of Physical Therapy, Imam

Haboubi, N. (2010). Assessment and management of nutrition in older people and its importance to health. Clinical Interventions in Aging, 207. https://doi.org/10.2147/CIA.S9664 DOI: https://doi.org/10.2147/CIA.S9664

Kuo, S.-E., Lai, H.-S., Hsu, J.-M., Yu, Y.-C., Zheng, D.-Z., & Hou, T.-W. (2018). A clinical nutritional information system with personalized nutrition assessment. Computer Methods and Programs in Biomedicine, 155, 209–216. https://doi.org/10.1016/j.cmpb.2017.10.029 DOI: https://doi.org/10.1016/j.cmpb.2017.10.029

Sikalidis, A. K., Kristo, A. S., Reaves, S. K., Kurfess, F. J., DeLay, A. M., Vasilaky, K., & Donegan, L. (2022). Capacity Strengthening Undertaking—Farm Organized Response of Workers against Risk for Diabetes: (C.S.U.—F.O.R.W.A.R.D. with Cal Poly)—A Concept Approach to Tackling Diabetes in Vulnerable and Underserved Farmworkers in California. Sensors, 22(21), 8299. https://doi.org/10.3390/s22218299 DOI: https://doi.org/10.3390/s22218299

Tanumihardjo, S. A., Anderson, C., Kaufer-Horwitz, M., Bode, L., Emenaker, N. J., Haqq, A. M., Satia, J. A., Silver, H. J., & Stadler, D. D. (2007). Poverty, Obesity, and Malnutrition: An International Perspective Recognizing the Paradox. Journal of the American Dietetic Association, 107(11), 1966–1972. https://doi.org/10.1016/j.jada.2007.08.007 DOI: https://doi.org/10.1016/j.jada.2007.08.007

Theodore Armand, T. P., Nfor, K. A., Kim, J.-I., & Kim, H.-C. (2024). Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review. Nutrients, 16(7), 1073. https://doi.org/10.3390/nu16071073 DOI: https://doi.org/10.3390/nu16071073