ArticlePloS one2025
The sports nutrition knowledge of large language model (LLM) artificial intelligence (AI) chatbots: An assessment of accuracy, completeness, clarity, quality of evidence, and test-retest reliability.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Navigating the AI era in dietetics: a qualitative analysis of professional identity, ethical concerns, and future projections in Türkiye.BMC health services research · 2026Article
- Review
- Large language models for late-life depression: a blinded benchmark of clinical safety, geriatric appropriateness, and triage.Frontiers in psychiatry · 2026Article
- Evaluating sports nutrition advice provided by general-purpose large language models for recreational female marathon runners: a multidimensional descriptive audit.Frontiers in nutrition · 2026Article
- The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming.Biology of sport · 2026Article
- Safety, accuracy, empathy, reliability, and readability of large language model chatbot responses to public-facing vegetarian and vegan nutrition advice questions: a cross-sectional comparative study.Frontiers in public health · 2026Article
- Generative artificial intelligence and large language models in sports medicine: a scoping review of applications, accuracy, and ethical implications.Frontiers in public health · 2026Article
- Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenerative artificial intelligence (AI) chatbots are increasingly utilised in various domains, including sports nutrition. Despite their growing popularity, there is limited evidence on the accuracy, completeness, clarity, evidence quality, and test-retest reliability of AI-generated sports nutrition advice. This study evaluates the performance of ChatGPT, Gemini, and Claude's basic and advanced models across these metrics to determine their utility in providing sports nutrition information. MATERIALS AND
methodsTwo experiments were conducted. In Experiment 1, chatbots were tested with simple and detailed prompts in two domains: Sports nutrition for training and Sports nutrition for racing. Intraclass correlation coefficient (ICC) was used to assess interrater agreement and chatbot performance was assessed by measuring accuracy, completeness, clarity, evidence quality, and test-retest reliability. In Experiment 2, chatbot performance was evaluated by measuring the accuracy and test-retest reliability of chatbots' answers to multiple-choice questions based on a sports nutrition certification exam. ANOVAs and logistic mixed models were used to analyse chatbot performance.
resultsIn Experiment 1, interrater agreement was good (ICC = 0.893) and accuracy varied from 74% (Gemini1.5pro) to 31% (ClaudePro). Detailed prompts improved Claude's accuracy but had little impact on ChatGPT or Gemini. Completeness scores were highest for ChatGPT-4o compared to other chatbots, which scored low to moderate. The quality of cited evidence was low for all chatbots when simple prompts were used but improved with detailed prompts. In Experiment 2, accuracy ranged from 89% (Claude3.5Sonnet) to 61% (ClaudePro). Test-retest reliability was acceptable across all metrics in both experiments.
conclusionsWhile generative AI chatbots demonstrate potential in providing sports nutrition guidance, their accuracy is moderate at best and inconsistent between models. Until significant advancements are made, athletes and coaches should consult registered dietitians for tailored nutrition advice.
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