ArticleNutrients2024
Is ChatGPT an Effective Tool for Providing Dietary Advice?
Article in Nutrients, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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.
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Who cites it
38 citing papers in PubMed.
- Evaluating and Validating Large Language Models for Health Education on Developmental Dysplasia of the Hip: 2-Phase Study With Expert Ratings and a Pilot Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- Generative AI Engagement and Perceived Nutrition-Oriented Feeding Practices Among Urban Indonesian Mothers: Mixed Methods Study.JMIR pediatrics and parenting · 2026Article
- Article
- Professional burnout among dietitians and the perceived role of artificial intelligence tools.Scientific reports · 2026Article
- Association between sources of nutrition information and self-perceived nutrition competence of healthcare professionals: a cross-sectional study.BMC medical education · 2026Article
- Validity and reliability of ChatGPT's responses on dietary supplements in Japan: A quality assessment and content analysis.PEC innovation · 2026Article
- Translating dietary standards into healthy meals with few-ingredient substitutions.PLOS digital health · 2026Article
- Using Large Language Models to Generate Dietary Feedback Similar to Human Experts in Weight Management: Experiments on Real-World Scenario Data.Bioengineering (Basel, Switzerland) · 2026Article
- Generative artificial intelligence-driven chatbots and medical misinformation: an accuracy, referencing and readability audit.BMJ open · 2026Article
- Development and validation of a tool for detecting misinformation risk in diet, nutrition, and health content (Diet-MisRAT).Scientific reports · 2026Article
- Artificial intelligence in prediabetes care: applications in screening, risk prediction, and lifestyle intervention.Frontiers in endocrinology · 2026Review
- Evaluating the appropriateness and safety of generative AI in delivering lifestyle guidance for atrial fibrillation patients.Scientific reports · 2025Article
- Article
- Accuracy of Large Language Model Responses Versus Internet Searches for Common Questions About Glucagon-Like Peptide-1 Receptor Agonist Therapy: Exploratory Simulation Study.JMIR formative research · 2025Article
- Feeding intelligence: comparative evaluation of ChatGPT and clinical guidelines for nutritional management in head and neck cancer.Journal of translational medicine · 2025Article
- Benchmarking ChatGPT and Other Large Language Models for Personalized Stage-Specific Dietary Recommendations in Chronic Kidney Disease.Journal of clinical medicine · 2025Article
- The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature.Nutrients · 2025Article
- Practical Guide to Nutritional Assessment in CKD: Current Tools, Emerging Technologies, and Future Directions.Clinical journal of the American Society of Nephrology : CJASN · 2025Review
- Feasibility analysis of generative artificial intelligence tools as a means of medical science popularization on urological diseases.Translational andrology and urology · 2025Article
- Artificial Intelligence in Nutrition and Dietetics: A Comprehensive Review of Current Research.Healthcare (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The chatbot Chat Generative Pretrained Transformer (ChatGPT) is becoming increasingly popular among patients for searching health-related information. Prior studies have raised concerns regarding accuracy in offering nutritional advice. We investigated in November 2023 ChatGPT's potential as a tool for providing nutritional guidance in relation to different non-communicable diseases (NCDs). First, the dietary advice given by ChatGPT (version 3.5) for various NCDs was compared with guidelines; then, the chatbot's capacity to manage a complex case with several diseases was investigated. A panel of nutrition experts assessed ChatGPT's responses. Overall, ChatGPT offered clear advice, with appropriateness of responses ranging from 55.5% (sarcopenia) to 73.3% (NAFLD). Only two recommendations (one for obesity, one for non-alcoholic-fatty-liver disease) contradicted guidelines. A single suggestion for T2DM was found to be "unsupported", while many recommendations for various NCDs were deemed to be "not fully matched" to the guidelines despite not directly contradicting them. However, when the chatbot handled overlapping conditions, limitations emerged, resulting in some contradictory or inappropriate advice. In conclusion, although ChatGPT exhibited a reasonable accuracy in providing general dietary advice for NCDs, its efficacy decreased in complex situations necessitating customized strategies; therefore, the chatbot is currently unable to replace a healthcare professional's consultation.
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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.