ArticlemHealth2026
Evaluation of AI-generated versus registered dietitian-authored nutrition responses: a cross-sectional study.
Article in mHealth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Domain-Dependent Performance of Human Experts and AI Systems in Pediatric Menu Evaluation.Nutrients · 2026Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) has emerged as a potential tool in nutrition counseling, but its performance compared with registered dietitians (RDs) remains unclear. This study aimed to compare the clinical quality, empathy, and readability of nutrition responses by a large language model (LLM, ChatGPT-4o) with those provided by licensed RDs, as assessed by RD evaluators. Methods: In this cross-sectional study, 100 nutrition-related questions were selected from public online forums where RDs had provided answers. Each question was paired with an AI-generated response. Licensed RDs (n=8), blinded to the source, rated responses for quality and empathy (5-point Likert scales) and overall performance (0-100). Readability was assessed using the Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), syllables per word, and words per sentence. Statistical analyses included independent two-tailed t-tests, z-tests for proportions meeting a threshold for "acceptable" (≥4), Pearson correlations, and sensitivity analyses by response length. Results: AI-generated responses scored higher than RD-authored responses for quality (4.48±0.31 Conclusions: AI-generated nutrition responses demonstrated consistently high perceived quality and empathy, independent of length, while RD-authored responses display greater variability but higher readability. These findings highlight both the promise and the limitations of LLMs in nutrition counseling, suggesting that AI may complement, but not replace, human expertise, provided that accuracy, transparency, and professional oversight are maintained.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.