Evaluation of AI-generated versus registered dietitian-authored nutrition responses: a cross-sectional study

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: 1.73±0.1 syllables/word, P