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Dietary guidance for pregnant women using DeepSeek-R1 and ChatGPT-4.0

A comparative analysis

Bibliographic Data

ID22089682
AuthorsZeJun Gao (Wenzhou Medical University, corresponding author), Jie Li (0000-0002-5131-4769, Wenzhou Medical University), WeiYue Fang (Wenzhou Medical University, corresponding author)
Year2026
Volume14
Pages1728954-1728954
Publication date2026-02-03
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2026.1728954
PMID41710315
OpenAlexW7127457357
LanguageEN
References cited36

Background Advancements in artificial intelligence (AI) and natural language processing have enabled the widespread application of large language models. However, the ability of AI models to provide dietary guidance for pregnant women remains unclear. This study aims to explore the capabilities of DeepSeek-R1 and ChatGPT-4.0 in generating dietary plans for pregnant women with different activity levels. Methods Personalized diet plans were generated using DeepSeek-R1 and ChatGPT-4.0. Through calorie calculation, Diet Quality Index-International (DQI-I) assessment, and cost analysis, the dietary quality and cost performance were evaluated. Results The requested caloric targets in DeepSeek’s diet plans were superior to those of ChatGPT. All plans achieved a satisfactory DQI-I score (≥ 70). The “adequacy” score of DeepSeek-R1 was much higher (DeepSeek-R1 35.8 ± 0.7 vs. ChatGPT-4.0 33.9 ± 0.8, p < 0.001), while ChatGPT-4.0 performed better in the “moderation” aspect (ChatGPT-4.0 22.3 ± 2.2 vs. DeepSeek-R1 17.0 ± 3.4, p = 0.004). ChatGPT-4.0 demonstrated better performance in terms of cost-effectiveness ( p = 0.017). Conclusion This study shows that DeepSeek-R1 and ChatGPT-4.0 can be helpful in providing personalized and reasonable dietary advice for pregnant women. In some aspects, such as food type adequacy, the emerging model “DeepSeek” performs better than ChatGPT

Maternal health · MEDLINE · Nutritional epidemiology · Population · Pregnancy · Gestational Diabetes Research and Management · Nutrition, Genetics, and Disease · Nutritional Studies and Diet

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