• Ilias Papastratis, Andreas Stergioulas, Dimitrios Konstantinidis, Petros Daras, and Kosmas Dimitropoulos.
• The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece. Electronic address: papastrat@iti.gr.
• Nutrition. 2024 May 1; 121: 112291112291.

ObjectivesDietary 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.MethodsUsing 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.ResultsExperimental results with different user profiles revealed the potential of ChatGPT models to provide personalized nutritional advice.ConclusionAdditional supervision and guidance by nutrition experts or knowledge-based systems are required to ensure meal appropriateness for users with NCDs.Copyright © 2023 Elsevier Inc. All rights reserved.

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