Digital Nutrition Care Ecosystem: Integrating Critical AI Nutrition Literacy, Countering Nutrition Misinformation, and Dietitian-Led Care

Chatbots and digital tools can make nutrition information more accessible, yet fluent explanations and precise-looking nutrient estimates may conceal unverified assumptions, portion-size errors, and inconsistent outputs. However, few frameworks connect critical appraisal of such outputs with routine clinical nutrition workflows and education. Building on a comparison of dietitian and chatbot interpretations of ready-to-eat meal labels, we propose the Digital Nutrition Care Ecosystem (DNCE), a human-supervised, Nutrition Care Process-aligned framework that links clinical nutrition workflows with critical AI nutrition literacy. DNCE connects eight functions: data acquisition, data integration, intelligence, nutrition diagnosis and clinical decision-making, individualized medical nutrition therapy, food implementation, delivery and engagement, and monitoring and learning. Within this ecosystem, dietitians are not replaced: repetitive, reversible, verifiable tasks may be supported by automation, whereas interpretation, individualized decisions, counseling, and final authorization remain professionally supervised. The paper contributes a proposed five-question educational checklist addressing (1) information source, (2) serving size, (3) assumptions and uncertainty, (4) consistency and verification, and (5) escalation before consequential action. Expected benefits include better recognition of unsupported nutrition claims, appropriate help-seeking, less correction work, and more dietitian time for judgment-intensive care. DNCE and the checklist are conceptual proposals that require empirical validation.