@article{Nasb2026, 
author = {Mohammad Nasb and Ying Zhang and Ning Chen},
title = {The role of artificial intelligence in precision exercise nutrition: a shift from data to diets},
year = {2026},
journal = {Food Science and Human Wellness},
volume = {15},
number = {3},
pages = {9250623},
keywords = {Artificial intelligence, Precision exercise nutrition, Exercise performance, Dietary recommendation, Multi-omics technology, Wearable device},
url = {https://www.sciopen.com/article/10.26599/FSHW.2025.9250623},
doi = {10.26599/FSHW.2025.9250623},
abstract = {The integration of artificial intelligence (AI) in precision exercise nutrition is reshaping how athletes optimize their dietary intake for performance, recovery, and overall well-being. This article discusses the intersection of AI technologies in formulating precision nutrition strategies tailored to distinct physiological and metabolic requirements of athletes. AI-based mechanisms, such as real-time diet monitoring, continuous glucose monitoring, and nutrient optimization systems, offer unique insights into the impact of AI on advancing precision nutrition applications through the involvement in analyzing complex datasets, merging genetic, metabolic and environmental factors, thereby contributing precise dietary recommendations that adjust to evolving necessities of an athlete. However, the obstacles presented by AI in this domain, including ethical considerations, data confidentiality, and the necessity for uniformity across diverse populations are also confronted. By using AI, athletes can attain greater precision in their nutrition plans, ultimately enhancing exercise performance and promoting fatigue or injury recovery in ways that traditional methods cannot rival. This article further culminates with an address on future trends, emphasizing the role of AI in boosting precision nutrition engagement for athletes, even common exercise enthusiasts.}
}