ArticleJournal of nutrition and metabolism2023
Comparison of Answers between ChatGPT and Human Dieticians to Common Nutrition Questions.
Article in Journal of nutrition and metabolism, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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
22 citing papers in PubMed, 55 citations in OpenAlex.
- Perceptions of AI-generated informational texts and effects of the source: An online experiment.Applied psychology. Health and well-being · 2026Article
- The effect of education provided to university students on sustainable nutrition: a comparison of ChatGPT and individual nutrition education.Scientific reports · 2026Article
- Review
- Professional burnout among dietitians and the perceived role of artificial intelligence tools.Scientific reports · 2026Article
- Machine Learning and Artificial Intelligence in Nutrition Research: Analytical Methods, Applications, and Key Considerations.The Journal of nutrition · 2026Review
- Validity and reliability of ChatGPT's responses on dietary supplements in Japan: A quality assessment and content analysis.PEC innovation · 2026Article
- ChatGPT Outperforms Personal Trainers in Answering Common Exercise Training Questions.Journal of sports science & medicine · 2026Article
- Article
- Feeding intelligence: comparative evaluation of ChatGPT and clinical guidelines for nutritional management in head and neck cancer.Journal of translational medicine · 2025Article
- The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature.Nutrients · 2025Article
- Evaluating Large Language Models and Retrieval-Augmented Generation Enhancement for Delivering Guideline-Adherent Nutrition Information for Cardiovascular Disease Prevention: Cross-Sectional Study.Journal of medical Internet research · 2025Article
- Evaluating the quality of online fertility nutrition claims.Public health nutrition · 2025Article
- AI-driven patient support: Evaluating the effectiveness of ChatGPT-4 in addressing queries about ovarian cancer compared with healthcare professionals in gynecologic oncology.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- From bytes to bites: application of large language models to enhance nutritional recommendations.Clinical kidney journal · 2025Review
- ChatGPT-4o and 4o1 Preview as Dietary Support Tools in a Real-World Medicated Obesity Program: A Prospective Comparative Analysis.Healthcare (Basel, Switzerland) · 2025Article
- Assessing ChatGPT's Role in Sarcopenia and Nutrition: Insights from a Descriptive Study on AI-Driven Solutions.Journal of clinical medicine · 2025Article
- Evaluation of LLMs accuracy and consistency in the registered dietitian exam through prompt engineering and knowledge retrieval.Scientific reports · 2025Article
- Article
- Large language models in clinical nutrition: an overview of its applications, capabilities, limitations, and potential future prospects.Frontiers in nutrition · 2025Review
- Is ChatGPT an Effective Tool for Providing Dietary Advice?Nutrients · 2024Article
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 at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: More people than ever seek nutrition information from online sources. The chatbot ChatGPT has seen staggering popularity since its inception and may become a resource for information in nutrition. However, the adequacy of ChatGPT to answer questions in the field of nutrition has not been investigated. Thus, the aim of this research was to investigate the competency of ChatGPT in answering common nutrition questions. Methods: Dieticians were asked to provide their most commonly asked nutrition questions and their own answers to them. We then asked the same questions to ChatGPT and sent both sets of answers to other dieticians ( Results: The overall grades for ChatGPT were higher than those from the dieticians for the overall scores in five of the eight questions we received. ChatGPT also had higher grades on five occasions for scientific correctness, four for actionability, and five for comprehensibility. In contrast, none of the answers from the dieticians had a higher average score than ChatGPT for any of the questions, both overall and for each of the grading components. Conclusions: Our results suggest that ChatGPT can be used to answer nutrition questions that are frequently asked to dieticians and provide encouraging support for the role of chatbots in offering nutrition support.
Identifiers
What OpenQuestion holds
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.