Exploring the Impact of Artificial Intelligence on Personalized Nutrition – A Systematic Literature Review
Abstract
With the rapid evolvement of the field of nutrition science, personalized nutrition has become a very promising strategy for managing the complexity of unique and individual dietary requirements. This systematic literature review examines the significant implications of integrating artificial intelligence into the promising field of personalized nutrition. The primary objective of this review is to assess how AI, encompassing a spectrum of technologies, can revolutionize the capacity to customize and personalize diet-related recommendations and interventions.
This review critically evaluates a diverse range of studies that highlight AI's role in enhancing personalized dietary recommendations, predicting health outcomes, including, weight loss, cardiovascular health, and other conditions based on personalized nutrition. The synthesis of empirical evidence highlights AI's predictive capabilities in forecasting health-related parameters, thus enabling early disease detection and personalized interventions.
Furthermore, the integration of AI in nutrient analysis and tracking offers insights into dietary behaviours and their influence on health. Ethical considerations, data privacy, and other challenges are also discussed as integral aspects of AI-driven personalized nutrition.
This review establishes the importance of interdisciplinary collaboration between nutritionists, data scientists, healthcare providers, and technologists. It advocates the necessity for clear regulatory frameworks in the dynamic landscape of AI applications in healthcare, particularly personalized nutrition.
The findings underscore that AI is positioned to be a pivotal tool in optimizing personalized nutrition, thus enhancing, and addressing the various factors influencing individual well-being. This systematic exploration highlights AI's transformative role in the evolving field of personalized nutrition.
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