This course is included in the NUTRITION 2026 On Demand Collection.

AI is reshaping dietary assessment and point-of-decision coaching, but its value hinges on validity, transparency, and safeguards. This session brings together experts in computer vision, behavioral nutrition, and implementation science to map the current evidence, compare AI vs. human/ground-truth methods, and outline best practices for model training, testing, and privacy. Speakers will address academic prototypes and commercial tools, dataset curation, guardrails for adaptive systems, and international use cases for real-time feedback.

Presentations will include speakers addressing (1) an introduction to Nutrition AI and academic pipelines, (2) adaptive AI, training, and guardrails, (3) U.S. and global applications with real-time feedback, and (4) a landscape review and academia–industry apps.

Originally presented at NUTRITION 2026, ASN's flagship meeting which was held July 25-28 in National Harbor, MD

Speakers

Lauren Au, PhD, RDN
Associate Professor
University of California, Davis

Debbie Fetter, PhD
Associate Professor of Teaching
University of California, Davis

Erin Hennessy, PhD, MPH 
Associate Professor and Dean for Research Strategy
Tufts University Friedman School of Nutrition Science and Policy

Diana Thomas, PhD
Professor, Department of Mathematical Sciences
West Point

Catherine Ward, PhD, RD
Clinical Lead, Nutrition Science, Adjunct Professor
Google, Stanford University

Fengqing Zhu, PhD
Associate Professor, Elmore Family School of Electrical and Computer Engineering
Purdue University