Artificial Intelligence is playing an increasingly important role in advancing Nutritional Studies by providing innovative solutions to complex research challenges. The role of artificial intelligence in nutritional studies enables the processing and analysis of large datasets, allowing for more accurate modeling of how nutrients affect metabolism and disease progression. In nutritional epidemiology, AI helps identify trends in diet patterns and their associations with chronic diseases. Furthermore, AI-powered technologies like predictive analytics can assist in creating personalized dietary interventions that optimize health based on individual profiles, such as genetics, lifestyle, and health history. As AI continues to evolve, it is poised to revolutionize nutritional science, offering precise and data-driven approaches to improving global health outcomes and combating nutrition-related diseases.
Title : Nutrients and bioactive compounds of non-traditional green leafy vegetables: A natural path to better health
Safiullah Pathan, Lincoln University of Missouri, United States
Title : Assessment of a Metabolic Map (MM) in association with Metabolic Syndrome (MS)
Antonio Claudio Goulart Duarte, Medicine School of The Federal University of Rio de Janeiro, Brazil
Title : The software tools for FOP nutrition labelling
Vintila Iuliana, University ”Dunarea de Jos” Galati, Romania
Title : Translation modulators to preserve neurodegenerative decline from metal toxicity
Jack Timothy Rogers, Harvard University, United States
Title : Effective methods for teaching artisanal skills: Lessons from custom cake workshops and professional kitchen training
Chetanya Rai, Tavistock Group, United States
Title : Risk factors for neural tube defects in conflict-impacted Tigray, Ethiopia: Findings from a case–control study
Tafere Gebreegziabher Belay, Central Washington University, United States