Harnessing AI in Lipid Nutrition: showing the course for future innovations

Nutrition science explores the impact of diet on health, formulates guidelines for public health, produces nutritious food products, and establishes health claims. The field uses various research methods, such as in vitro studies, clinical trials, nutrition surveys, and systematic reviews.

Artificial Intelligence (AI) is emerging as a crucial tool in nutrition and public health, offering valuable insights into the impact of dietary lipids on health by analyzing extensive datasets from diverse sources.

AI can enhance understanding of lipids at molecular, individual, and population levels, improving personalized nutrition, tracking health trends in lipids-related issues, and addressing public health challenges related to lipids and nutrition globally.

AI methodologies are well-positioned to be integrated into nutrition research and practice. AI technologies can assist with 1) data collection, processing, and analysis, 4) monitoring the nutritional status of populations 3) elucidating mechanisms of action 2) predicting the health effects of diets, nutrients, and bioactive compounds. Furthermore, AI can facilitate personalized nutrition recommendations tailored to individual needs.

The aim of this abstract is to summarize the current state of AI applications in lipid nutrition and explore prospects across various scales, from biochemical perspectives to product development and public health implications. AI tools can expedite knowledge generation and decision-making by dismantling knowledge silos, and enabling the practical application of generated knowledge (e.g., in policies, guidelines, recommendations, and product development).