Nutrition (Burbank, Los Angeles County, Calif.) · 2025
Comparison of ChatGPT-3.5, ChatGPT-4.0 and DeepSeek in generating dietary plans for patients with chronic kidney disease: A focus on nutritional accuracy and dietary inflammation.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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You Qian, Zhou Linhua, Ma Ya, Guo Jiankui, Wang Yi, Shi Lei, Deng Yanru, Rao Zhiyong, Li Xuemei
Аннотация
BACKGROUND: Our previous research indicated that ChatGPT-3.5 was inadequate in generating nutritionally accurate dietary plans for patients with chronic kidney disease (CKD). Given the subsequent release of ChatGPT-40 and the emergence of DeepSeek, the aim of this study is to assess and compare the CKD dietary plans generated by ChatGPT-3.5, ChatGPT-4.0, and DeepSeek in terms of both nutritional accuracy and the Dietary Inflammatory Index (DII).
METHODS: Standardized dietary prescription prompts with a fixed energy target of 1800 kcal and five protein levels (30 g, 40 g, 50 g, 60 g, and 70 g) were designed to represent different stages of CKD. Twenty participants were recruited and instructed to input these pre-established prompts into the three LLMs to generate renal diets. The actual energy and protein contents of the resulting meal plans were recorded and compared across the three models. The DII values were calculated based on the USDA database.
RESULTS: A total of 300 meal plans were generated. All three LLMs failed to meet the 1800 kcal energy target, with values ranging from 1165 to 1548 kcal (P < 0.001). DeepSeek generated plans that were closest to the target energy and protein levels. ChatGPT-4.0 generated the most anti-inflammatory plans (DII = -1.487335), followed by ChatGPT-3.5 (DII = -0.9163290); whereas DeepSeek (DII = 2.2502160) produced pro-inflammatory plans.
CONCLUSION: This study confirmed the limitations of LLMs in generating nutritionally accurate dietary plans for patients with CKD. DeepSeek outperformed the ChatGPT models in nutritional accuracy, whereas the ChatGPT models generated more anti-inflammatory diets. As AI evolves, human oversight remains crucial in ensuring the safety and efficacy of AI-generated dietary plans.
METHODS: Standardized dietary prescription prompts with a fixed energy target of 1800 kcal and five protein levels (30 g, 40 g, 50 g, 60 g, and 70 g) were designed to represent different stages of CKD. Twenty participants were recruited and instructed to input these pre-established prompts into the three LLMs to generate renal diets. The actual energy and protein contents of the resulting meal plans were recorded and compared across the three models. The DII values were calculated based on the USDA database.
RESULTS: A total of 300 meal plans were generated. All three LLMs failed to meet the 1800 kcal energy target, with values ranging from 1165 to 1548 kcal (P < 0.001). DeepSeek generated plans that were closest to the target energy and protein levels. ChatGPT-4.0 generated the most anti-inflammatory plans (DII = -1.487335), followed by ChatGPT-3.5 (DII = -0.9163290); whereas DeepSeek (DII = 2.2502160) produced pro-inflammatory plans.
CONCLUSION: This study confirmed the limitations of LLMs in generating nutritionally accurate dietary plans for patients with CKD. DeepSeek outperformed the ChatGPT models in nutritional accuracy, whereas the ChatGPT models generated more anti-inflammatory diets. As AI evolves, human oversight remains crucial in ensuring the safety and efficacy of AI-generated dietary plans.