UCSF

Science-informed decisions are best guided by the objective synthesis of the totality of evidence around a particular question and assessing its trustworthiness through systematic processes. However, there are major barriers and challenges that limit science-informed food and nutrition policy, practice, and guidance. First, insufficient evidence, primarily due to acquisition cost of generating high-quality data, and the complexity of the diet-disease relationship. Furthermore, the sheer number of systematic reviews needed across the entire agriculture and food value chain, and the cost and time required to conduct them, can delay the translation of science to policy. Artificial intelligence offers the opportunity to (i) better understand the complex etiology of diet-related chronic diseases, (ii) bring more precision to our understanding of the variation among individuals in the diet-chronic disease relationship, (iii) provide new types of computed data related to the efficacy and effectiveness of nutrition/food interventions in health promotion, and (iv) automate the generation of systematic reviews that support timely decisions. These advances include the acquisition and synthesis of heterogeneous and multimodal datasets. This perspective summarizes a meeting convened at the National Academy of Sciences, Engineering, and Medicine. The purpose of the meeting was to examine the current state and future potential of artificial intelligence in generating new types of computed data as well as automating the generation of systematic reviews to support evidence-based food and nutrition policy, practice, and guidance.