AI-Driven Precision Nutrition: Advancing Personalized Dietary Systems for Public Health Equity in Resource-Constrained Environments

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 15 | Issue : 01 | Page :
    By

    David Sunday Araoti,

  1. Independent Researcher, Policy Practitioner | AI & Governance Specialist Oyo State,Policy Practitioner AI & Governance Specialist, Oyo State, Nigeria

Abstract

The dual burden of malnutrition and diet-related non-communicable diseases (NCDs) represents a growing global public health challenge, particularly in low- and middle-income countries. Traditional dietary guidelines are largely population-based and fail to account for individual variability in genetics, metabolism, lifestyle, and environmental exposure. This limitation has led to the emergence of precision nutrition, an evolving field that integrates biological data and computational intelligence to deliver personalized dietary recommendations. This paper proposes an AI-driven precision nutrition framework designed to enhance public health equity in resource-constrained environments. The framework integrates artificial intelligence (AI), nutrigenomics, metabolomics, and behavioral data to generate individualized dietary interventions aimed at preventing and managing NCDs such as obesity, diabetes, and cardiovascular diseases. Machine learning algorithms are conceptualized as the core analytical engine, capable of processing multidimensional health datasets to identify dietary patterns and predict nutritional outcomes. The study further explores the feasibility of implementing such systems in low-resource settings through mobile health platforms, cloud-based analytics, and community-level data collection systems. Emphasis is placed on scalability, affordability, and accessibility to ensure inclusivity in healthcare innovation. Ethical considerations, including data privacy, algorithmic fairness, and equitable access to personalized nutrition technologies, are critically examined. Findings from the conceptual analysis suggest that AI-driven precision nutrition has the potential to transform dietary interventions from generalized recommendations to adaptive, data-driven systems that respond to individual health profiles. However, challenges such as data scarcity, infrastructural limitations, and socio-economic disparities must be addressed to ensure successful implementation. The paper contributes to the growing field of digital nutrition science and precision public health by presenting a scalable framework that aligns advanced computational methods with real-world nutritional challenges in underserved populations. It ultimately argues that AI-enabled dietary systems can play a transformative role in reducing global health inequities and improving long-term population health outcomes.

Keywords: Artificial Intelligence; Precision Nutrition; Nutrigenomics; Metabolomics; Personalized Diet; Public Health Equity; Machine Learning; Digital Health; Non-Communicable Diseases; Resource-Constrained Environments

[This article belongs to Research and Reviews : A Journal of Medical Science and Technology ]

How to cite this article:
David Sunday Araoti. AI-Driven Precision Nutrition: Advancing Personalized Dietary Systems for Public Health Equity in Resource-Constrained Environments. Research and Reviews : A Journal of Medical Science and Technology. 2026; 15(01):-.
How to cite this URL:
David Sunday Araoti. AI-Driven Precision Nutrition: Advancing Personalized Dietary Systems for Public Health Equity in Resource-Constrained Environments. Research and Reviews : A Journal of Medical Science and Technology. 2026; 15(01):-. Available from: https://journals.stmjournals.com/rrjomst/article=2026/view=241828


References

  1. Afshin, A., Sur, P. J., Fay, K. A., et al. (2019). Health effects of dietary risks in 195 countries, 1990–2017. The Lancet, 393(10184), 1958–1972.
  2. Bansal, S., Goodman, J., & Rodriguez, B. (2020). Artificial intelligence in precision medicine and nutrition. Nature Medicine, 26(1), 45–53.
  3. Berry, S. E., Valdes, A. M., & Drew, D. A. (2020). Human postprandial responses to food and potential for precision nutrition. Nature Medicine, 26(6), 964–973.
  4. Blossom, J., et al. (2018). Digital health technologies and personalized nutrition systems. Annual Review of Nutrition, 38, 1–25.
  5. Corella, D., & Ordovas, J. M. (2018). Nutrigenomics in cardiovascular disease prevention. Current Opinion in Lipidology, 29(1), 1–8.
  6. De Roos, B., & Brennan, L. (2017). Personalised interventions in nutrition. Proceedings of the Nutrition Society, 76(4), 579–589.
  7. Duarte, C. M., et al. (2021). Machine learning applications in dietary prediction. Briefings in Bioinformatics, 22(3), 1–15.
  8. Floridi, L., et al. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28, 689–707.
  9. Gao, P., et al. (2021). Deep learning in healthcare: applications and challenges. The Lancet Digital Health, 3(10), e659–e666.
  10. Gibney, M. J. (2019). Nutrition science and the future of personalized diet. American Journal of Clinical Nutrition, 110(3), 467–472.
  11. Gomez-Cabrero, D., et al. (2014). Systems biology and metabolomics integration. Briefings in Bioinformatics, 15(3), 433–445.
  12. Huang, S., Chaudhary, K., & Garmire, L. X. (2017). Machine learning for metabolomics. Frontiers in Genetics, 8, 1–10.
  13. Jain, M., et al. (2022). Metabolomics and human health. Nature Reviews Molecular Cell Biology, 23(2), 123–140.
  14. Johnson, K. W., et al. (2016). Metabolomics: beyond biomarkers. Nature Reviews Molecular Cell Biology, 17(7), 451–459.
  15. Koplin, J. J., et al. (2020). Digital nutrition and public health. The Lancet Public Health, 5(10), e514–e522.
  16. Li, X., et al. (2020). AI-driven dietary assessment systems. Computers in Biology and Medicine, 120, 103722.
  17. Manrai, A. K., et al. (2016). Genetic mismatch in clinical predictions. Science, 353(6303), 1543–1545.
  18. Ordovas, J. M., & Ferguson, L. R. (2017). Nutrigenomics and personalized diet. Genes & Nutrition, 12(1), 1–12.
  19. Patel, V., et al. (2021). Digital health systems in low-resource settings. BMJ Global Health, 6(3), e004332.
  20. Qi, L. (2020). Precision nutrition: from science to practice. Cell, 181(1), 23–25.
  21. Rappaport, S. M., & Smith, M. T. (2010). Environment and disease in metabolomics. Environmental Health Perspectives, 118(8), 1095–1101.
  22. Schmidt, H., et al. (2020). Ethical issues in AI healthcare systems. The Lancet Digital Health, 2(9), e506–e512.
  23. Snyder, M. P., et al. (2019). Personalized nutrition using multi-omics data. Cell, 177(1), 171–183.
  24. Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
  25. Wang, D., et al. (2020). AI in nutritional epidemiology. Nature Food, 1(12), 737–748.
  26. WHO (2023). Noncommunicable diseases: Key facts. World Health Organization.
  27. Wishart, D. S. (2019). Metabolomics for precision health. Nature Reviews Drug Discovery, 18(7), 463–476.
  28. Zeevi, D., et al. (2015). Personalized nutrition by prediction of glycemic responses. Cell, 163(5), 1079–1094.
  29. Zhou, X., & Xu, Y. (2021). Machine learning in nutrition science. Trends in Food Science & Technology, 110, 254–266.
Regular Issue Subscription Review Article
Volume 15
Issue 01
Received 18/04/2026
Accepted 26/04/2026
Published 29/04/2026
Publication Time 11 Days
7

Login


My IP

PlumX Metrics