ArticleEuropean journal of clinical nutrition2024
Artificial intelligence chatbots for the nutrition management of diabetes and the metabolic syndrome.
Article in European journal of clinical nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large language models in obesity: a systematic review.International journal of obesity (2005) · 2026Pooled it
- The effect of education provided to university students on sustainable nutrition: a comparison of ChatGPT and individual nutrition education.Scientific reports · 2026Article
- Can Artificial Intelligence Chatbots Plan Therapeutic Ketogenic Diets for Children With Epilepsy?Journal of human nutrition and dietetics : the official journal of the British Dietetic Association · 2026Article
- Assessment of large language model chatbots for hemodialysis meal planning: a descriptive study.BMC nephrology · 2026Article
- Precision nutrition in diabetic foot ulcers: multimodal artificial intelligence for personalized metabolic management.Frontiers in nutrition · 2026Review
- Evaluation of AI-generated versus registered dietitian-authored nutrition responses: a cross-sectional study.mHealth · 2026Article
- The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming.Biology of sport · 2026Article
- Artificial intelligence diet plans underestimate nutrient intake compared to dietitians in adolescents.Frontiers in nutrition · 2026Article
- 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
- Article
- Applications of large language models in cardiovascular disease: a systematic review.European heart journal. Digital health · 2025Review
- The Influence of an AI-Driven Personalized Nutrition Program on the Human Gut Microbiome and Its Health Implications.Nutrients · 2025Article
- Evaluation of LLMs accuracy and consistency in the registered dietitian exam through prompt engineering and knowledge retrieval.Scientific reports · 2025Article
- Diet Quality and Caloric Accuracy in AI-Generated Diet Plans: A Comparative Study Across Chatbots.Nutrients · 2025Article
- Investigation and Assessment of AI's Role in Nutrition-An Updated Narrative Review of the Evidence.Nutrients · 2025Review
- Comparison of the Accuracy, Completeness, Reproducibility, and Consistency of Different AI Chatbots in Providing Nutritional Advice: An Exploratory Study.Journal of clinical medicine · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecently, there has been a growing interest in exploring AI-driven chatbots, such as ChatGPT, as a resource for disease management and education.
objectiveThe study aims to evaluate ChatGPT's accuracy and quality/clarity in providing nutritional management for Type 2 Diabetes (T2DM), the Metabolic syndrome (MetS) and its components, in accordance with the Academy of Nutrition and Dietetics' guidelines.
methodsThree nutrition management-related domains were considered: (1) Dietary management, (2) Nutrition care process (NCP) and (3) Menu planning (1500 kcal). A total of 63 prompts were used. Two experienced dietitians evaluated the chatbot output's concordance with the guidelines.
resultsBoth dietitians provided similar assessments for most conditions examined in the study. Gaps in the ChatGPT-derived outputs were identified and included weight loss recommendations, energy deficit, anthropometric assessment, specific nutrients of concern and the adoption of specific dietary interventions. Gaps in physical activity recommendations were also observed, highlighting ChatGPT's limitations in providing holistic lifestyle interventions. Within the NCP, the generated output provided incomplete examples of diagnostic documentation statements and had significant gaps in the monitoring and evaluation step. In the 1500 kcal one-day menus, the amounts of carbohydrates, fat, vitamin D and calcium were discordant with dietary recommendations. Regarding clarity, dietitians rated the output as either good or excellent.
conclusionAlthough ChatGPT is an increasingly available resource for practitioners, users are encouraged to consider the gaps identified in this study in the dietary management of T2DM and the MetS.
Indexed as
Identifiers
39060542What 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.