ArticleCureus2025
AI-Generated Diet and Exercise Recommendations for Cardiovascular Health Compared to Established Cardiology Society Guidelines.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Large Language Models as Clinical Nutrition Decision Tools: Quantitative Bias and Guideline Deviation in Type 2 Diabetes Meal Planning.Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimAs numerous individuals turn to the internet for initial guidance, health literacy is crucial for establishing sound health practices within the broader society. Although diet and exercise are crucial for prevention, artificial intelligence (AI) models can offer accurate health information; cardiovascular disease (CVD) continues to be a significant source of morbidity and mortality. The increasing application of AI in healthcare necessitates a thorough evaluation of the efficacy of large language models (LLMs) in providing dependable health recommendations. This study aimed to assess the appropriateness, biases, and clinical relevance of diet and exercise recommendations produced by four prominent language models (ChatGPT {San Francisco, CA: OpenAI}, Claude AI {San Francisco, CA: Anthropic}, DeepSeek AI {Hangzhou, China: DeepSeek}, Google Gemini {Google LLC: Mountain View, CA}) in relation to established cardiovascular disease association guidelines from the American Heart Association/American College of Cardiology (AHA/ACC) and the European Society of Cardiology (ESC).
methodsA cross-sectional study was conducted using 15 standardized questions (five on physical activity, 10 on diet) evaluated by a primary care physician and a cardiology fellow. A cardiologist reviewed discrepancies in the evaluations by the two examiners; in such instances, the final grade was established by the median of the grades assigned by all three examiners. Based on compliance with AHA/ACC and ESC guidelines, responses were rated as appropriate, appropriate but insufficient, partially inappropriate, or entirely inappropriate.
resultsNinety percent of responses from ChatGPT, Claude AI, and DeepSeek AI met established cardiovascular health standards, indicating superior performance among the language models. All five recommendations for physical activity were deemed appropriate. Google Gemini had a performance level of 80%, while 90% of the outcomes from the three LLMs were suitable for nutritional guidance. Particularly concerning carbohydrate and added sugar intake, all models struggled to provide precise quantitative guidance. Exercise recommendations indicated a slight preference for AHA/ACC guidelines.
conclusionsWhile LLMs demonstrate potential for accessible health information sources, they cannot replace expert medical advice. This study highlights the need for continued medical professional interpretation and tailored healthcare guidance. Future advances should concentrate on raising the specificity of health recommendations and guaranteeing a fairer interpretation of international guidelines.
Indexed as
Identifiers
What Socratic 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 Socratic graph.