From plausible menus to trustworthy nutrition: a risk-proportionate VALID-Diet framework for artificial intelligence-generated dietary advice
- 1Department of Hepatobiliary and Pancreatic Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China
- 2Anesthesia and Surgery Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China
- 3Department of Liver Intensive Care Unit, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China
Abstract
Generative artificial intelligence can produce menus, recipes, shopping lists, and dietary explanations that appear individualized and clinically informed. Fluency alone, however, provides little evidence of nutritional accuracy, safety, feasibility, or benefit. We propose VALID-Diet, a staged framework for evaluating systems that generate actionable dietary recommendations as sociotechnical interventions. Its domains are Verification of nutritional targets and composition; Adversarial safety and boundary testing; Linkage between user inputs and recommendation changes; Implementation feasibility and inclusion; and Documentation, versioning, and reproducibility. Stage 0 defines intended use, input integrity, and a structured risk profile based on harm severity, exposure, autonomy, reversibility, and enforceable oversight. Stage 1 separates target validity, menu achievement, and arithmetic agreement and, for integrated systems, tests retrieval, calculation, and generation as distinct components. Stage 2 evaluates safety, substantive personalization, feasibility, and inclusion using pre-specified case blueprints. Stage 3 evaluates human oversight, workflow utility, and prospective outcomes only after earlier gates are passed. A retrospective worked application to a published retrieval-augmented personalized-nutrition system illustrates risk assignment and progression decisions. VALID-Diet is not a regulatory standard or consensus guideline; it is a testable proposal intended to align research claims with evidentiary strength.
Related
- Artificial Intelligence in Otolaryngology—From Demonstrating Capability to Delivering Trustworthy Clinical Impact
- Patient expectations for artificial intelligence transparency and agency: A qualitative study and proportionate framework for clinical artificial intelligence implementation
- How far are vision-free medical robots from achieving trustworthy embodied intelligence?
- A focused PubMed artificial intelligence search program creates better literature reviews than general purpose artificial intelligence and is comparable to trainees in shoulder and elbow surgery
- Proportionate and disproportionate reporting of different infectious complications of disease modifying therapy in multiple sclerosis.
BibTeX
@article{xu2026from,
title = {{From plausible menus to trustworthy nutrition: a risk-proportionate VALID-Diet framework for artificial intelligence-generated dietary advice}},
author = {Jingyi Xu and Weina Xing and Lili Jia and Xi Li and Qijia Zhang and Wei Yang},
journal = {Frontiers in Nutrition},
year = {2026},
month = oct,
doi = {10.3389/fnut.2026.1957211},
url = {https://doi.org/10.3389/fnut.2026.1957211},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - From plausible menus to trustworthy nutrition: a risk-proportionate VALID-Diet framework for artificial intelligence-generated dietary advice
AU - Jingyi Xu
AU - Weina Xing
AU - Lili Jia
AU - Xi Li
AU - Qijia Zhang
AU - Wei Yang
PY - 2026
DA - 2026/10/13
JO - Frontiers in Nutrition
DO - 10.3389/fnut.2026.1957211
UR - https://doi.org/10.3389/fnut.2026.1957211
AB - Generative artificial intelligence can produce menus, recipes, shopping lists, and dietary explanations that appear individualized and clinically informed. Fluency alone, however, provides little evidence of nutritional accuracy, safety, feasibility, or benefit. We propose VALID-Diet, a staged framework for evaluating systems that generate actionable dietary recommendations as sociotechnical interventions. Its domains are Verification of nutritional targets and composition; Adversarial safety and boundary testing; Linkage between user inputs and recommendation changes; Implementation feasibility and inclusion; and Documentation, versioning, and reproducibility. Stage 0 defines intended use, input integrity, and a structured risk profile based on harm severity, exposure, autonomy, reversibility, and enforceable oversight. Stage 1 separates target validity, menu achievement, and arithmetic agreement and, for integrated systems, tests retrieval, calculation, and generation as distinct components. Stage 2 evaluates safety, substantive personalization, feasibility, and inclusion using pre-specified case blueprints. Stage 3 evaluates human oversight, workflow utility, and prospective outcomes only after earlier gates are passed. A retrospective worked application to a published retrieval-augmented personalized-nutrition system illustrates risk assignment and progression decisions. VALID-Diet is not a regulatory standard or consensus guideline; it is a testable proposal intended to align research claims with evidentiary strength.
SN - 2296-861X
ER -