The use of Artificial Intelligence and data analytics in nutrition

Papasani, Pavan kumar and Papasani, Ravi Teja (2025) The use of Artificial Intelligence and data analytics in nutrition. International Journal of Science and Research Archive, 15 (1). pp. 795-801. ISSN 2582-8185

The increasing use of data analytics in nutrition research symbolizes a major sea change in developing and personalizing dietary interventions. The big data, AI, and ML being applied to this field of nutrition will succeed in enabling more correct, personalized dietary advice considering the genetic predisposition to certain health conditions and the behavioral pattern of a person. Such technologies have the potential to analyze large-scale data sets that may reveal correlations between specific eating patterns and health outcomes that could not be found using traditional approaches. For instance, the different behaviors of clients with different body makeup may be predicted by ML when exposed to nutrition; making the dream of personalized nutrition possible. In addition, omics sciences/genomics, proteomics, and metabolomics applied in nutrition provide nutrition researchers with a more comprehensive view of how multiple nutrients affect genes, metabolism, and health in an overall sense. An approach like this has also proved useful in the management of chronic diseases like obesity, diabetes, and cardiovascular diseases among others due to the discovery of bioactive compounds present in foods that can help in the management or prevention of diseases. It also equally facilitates monitoring of diet in real-time using wearable technology and smartphone applications due to analytics. This in turn enables the right advice to be delivered to the right individual and this can change given the current state of the individual's health. Analytics also enables nutritionists or dieticians to adjust all parameters of nutrition individually to ensure that people achieve the best results in the given health goals, such as weight loss or management of chronic diseases. It also enhances food safety since the detection of various allergens, and other ingredients becomes precise using big data. Therefore, ethical issues are one of the development challenges that persist. Consumer protection – especially concerning sensitive health information – means strict measures on protocols, especially concerning consent to maintain consumers' faith. In general, it has been revealed that AI and ML in nutritional research provide a new horizon of an individual-centric health model of the future which is going to focus on finding the best possible solution for healthy living and disease prevention.