AI Advancements in Food, Nutrition and Dietetics: A Comprehensive Survey

As the field of nutrition grapples with increasingly complex and intricate datasets, there's a growing need for new, more effective methods of data analysis. It's becoming clear that Artificial Intelligence and machine learning hold great promise in simplifying this complexity. These technologies have already shown their potential in addressing critical nutritional issues like obesity, metabolic health, and malnutrition. However, there's a disconnect between AI and nutrition experts, which hinders the widespread use of AI to solve these pressing problems. To bridge this gap, we've taken a closer look at previous work in the field. The goal is to create AI tools that are user-friendly and accessible for nutrition researchers, making it easier for them to integrate AI into their research processes. This, in turn, can enhance our capacity to tackle unresolved nutritional issues. AI's application in nutrition sets it apart from current systems and offers a fresh perspective. Notably, there are several remarkable examples in the nutrition literature where AI has made a significant impact. We've delved into areas like metabolomics and precision nutrition, presenting them as case studies to illustrate how AI can be effectively applied. Furthermore, we've provided a comprehensive framework for researchers who wish to incorporate Artificial Intelligence into their nutritional studies, with the aim of becoming a valuable resource for those looking to harness the power of AI in the field of nutrition and dietetics.