The Role of Artificial Intelligence in Deciphering Diet–Disease Relationships: Case Studies

Abstract

Modernization of society from a rural, hunter-gatherer setting into an urban and industrial one, with the associated dietary changes, has led to an increased prevalence of cardiometabolic and additional noncommunicable diseases, such as cancer, inflammatory bowel disease, and neurodegenerative and autoimmune disorders. However, while dietary sciences have been rapidly evolving to meet these challenges, validation and translation of experimental results into clinical practice remain limited for multiple reasons, including inherent ethnic, gender, and cultural interindividual variability, among other methodological, dietary reporting–related, and analytical issues. Recently, large clinical cohorts with artificial intelligence analytics have introduced new precision and personalized nutrition concepts that enable one to successfully bridge these gaps in a real-life setting. In this review, we highlight selected examples of case studies at the intersection between diet–disease research and artificial intelligence. We discuss their potential and challenges and offer an outlook toward the transformation of dietary sciences into individualized clinical translation. Expected final online publication date for the Annual Review of Nutrition, Volume 43 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Digital Anti-Aging Healthcare: An Overview of the Applications of Digital Technologies in Diet Management

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Artificial Neural Network Algorithm in Nutritional Assessment: Implication for Machine Learning Prediction in Nutritional Assessments in Strict Veganism

“…Actually, when several well-followed dietary suggestions don't work over time, people become frustrated and try to figure out what to eat by trial and error, which may involve following dubious or non-evidence-based advice. Excessive consumption of non-caloric food additives with lower calorie content can cause blood glucose levels to unexpectedly rise when trying to control diabetes and lose weight (Cohen, Valdés-Mas, & Elinav, 2023). Dietary studies frequently fail to identify some of these negative effects because of insufficient data collection, a lack of long-term follow-up, or an inability to contact dropouts who may suffer these negative consequences.…”

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“…What about the generalizability of studies in PN context? In fact, in PN sometimes the data stem from a nonrepresentative subset [ 14 ] of an elite group of individuals, who have already high knowledge of nutrition and high capacity to implement dietary recommendations.…”