ArticleScientific reports2025
Evaluation of LLMs accuracy and consistency in the registered dietitian exam through prompt engineering and knowledge retrieval.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Generative AI in Precision Nutrition: A Review of Current Developments and Future Directions.Nutrients · 2026Pooled it
- Applying Artificial Intelligence and machine learning in precision nutrition.Nature communications · 2026Review
- Article
- Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow.STAR protocols · 2026Article
- Retrieval-Augmented Language Models for Clinical Decision Support in the Classification of Inborn Errors of Immunity.Journal of clinical immunology · 2026Article
- Digital twin-supported behavioral intention in mothers of young children to prevent childhood obesity: a large language model-based intervention study.Frontiers in artificial intelligence · 2026Article
- Comparing large language models and search engine responses to common orthodontic questions.PloS one · 2026Article
- A comparative study of vision-language models for food ingredient recognition and nutrient estimation.Current research in food science · 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
- Large Language Models for Real-World Nutrition Assessment: Structured Prompts, Multi-Model Validation and Expert Oversight.Nutrients · 2025Article
- Benchmarking ChatGPT and Other Large Language Models for Personalized Stage-Specific Dietary Recommendations in Chronic Kidney Disease.Journal of clinical medicine · 2025Article
- Performance Evaluation of 3 Large Language Models for Nutritional Content Estimation from Food Images.Current developments in nutrition · 2025Article
- Performance evaluation of GPT-4o on South Korean national exams for building mechanical equipment maintenance.Scientific reports · 2025Article
- Cognition-enhanced geospatial decision framework integrating fuzzy FCA, surprisingly popular method, and a large language model.Scientific reports · 2025Article
- Evaluating the Accuracy, Reliability, Consistency, and Readability of Different Large Language Models in Restorative Dentistry.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2025Article
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
- Retrieval augmented generation for large language models in healthcare: A systematic review.PLOS digital health · 2025Article
- Evaluating performance of large language models for atrial fibrillation management using different prompting strategies and languages.Scientific reports · 2025Article
- Performance of five free large language models in dental trauma: a 30-day longitudinal benchmark study.Frontiers in oral health · 2025Article
- The generative illusion: how ChatGPT-like AI tools could reinforce misinformation and mistrust in public health communication.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) are fundamentally transforming human-facing applications in the health and well-being domains: boosting patient engagement, accelerating clinical decision-making, and facilitating medical education. Although state-of-the-art LLMs have shown superior performance in several conversational applications, evaluations within nutrition and diet applications are still insufficient. In this paper, we propose to employ the Registered Dietitian (RD) exam to conduct a standard and comprehensive evaluation of state-of-the-art LLMs, GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, assessing both accuracy and consistency in nutrition queries. Our evaluation includes 1050 RD exam questions encompassing several nutrition topics and proficiency levels. In addition, for the first time, we examine the impact of Zero-Shot (ZS), Chain of Thought (CoT), Chain of Thought with Self Consistency (CoT-SC), and Retrieval Augmented Prompting (RAP) on both accuracy and consistency of the responses. Our findings revealed that while these LLMs obtained acceptable overall performance, their results varied considerably with different prompts and question domains. GPT-4o with CoT-SC prompting outperformed the other approaches, whereas Gemini 1.5 Pro with ZS recorded the highest consistency. For GPT-4o and Claude 3.5, CoT improved the accuracy, and CoT-SC improved both accuracy and consistency. RAP was particularly effective for GPT-4o to answer Expert level questions. Consequently, choosing the appropriate LLM and prompting technique, tailored to the proficiency level and specific domain, can mitigate errors and potential risks in diet and nutrition chatbots.
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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.