The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece.
The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece.
Nutrition. 2024 May;121:112291. doi: 10.1016/j.nut.2023.112291. Epub 2023 Nov 11.
Dietary habits significantly affect health conditions and are closely related to the onset and progression of non-communicable diseases (NCDs). Consequently, a well-balanced diet plays an important role in lessening the effects of various disorders, including NCDs. Several artificial intelligence recommendation systems have been developed to propose healthy and nutritious diets. Most of these systems use expert knowledge and guidelines to provide tailored diets and encourage healthier eating habits. However, new advances in large language models such as ChatGPT, with their ability to produce human-like responses, have led individuals to search for advice in several tasks, including diet recommendations. This study aimed to determine the ability of ChatGPT models to generate appropriate personalized meal plans for patients with obesity, cardiovascular diseases, and type 2 diabetes.
Using a state-of-the-art knowledge-based recommendation system as a reference, we assessed the meal plans generated by two large language models in terms of energy intake, nutrient accuracy, and meal variability.
Experimental results with different user profiles revealed the potential of ChatGPT models to provide personalized nutritional advice.
Additional supervision and guidance by nutrition experts or knowledge-based systems are required to ensure meal appropriateness for users with NCDs.
饮食习惯对健康状况有重大影响，与非传染性疾病（NCD）的发生和发展密切相关。因此，均衡的饮食对于减轻各种疾病（包括 NCD）的影响非常重要。已经开发了几种人工智能推荐系统来提出健康和有营养的饮食。这些系统大多使用专家知识和指南来提供量身定制的饮食，并鼓励更健康的饮食习惯。然而，像 ChatGPT 这样的大型语言模型的新进展，具有生成类人反应的能力，导致人们在包括饮食推荐在内的多项任务中寻求建议。本研究旨在确定 ChatGPT 模型为肥胖、心血管疾病和 2 型糖尿病患者生成合适的个性化膳食计划的能力。
我们使用最先进的基于知识的推荐系统作为参考，根据能量摄入、营养准确性和膳食多样性来评估两种大型语言模型生成的膳食计划。
不同用户资料的实验结果显示了 ChatGPT 模型为用户提供个性化营养建议的潜力。
需要由营养专家或基于知识的系统进行额外的监督和指导，以确保为患有 NCD 的用户提供适当的膳食。
