Can ChatGPT provide appropriate meal plans for NCD patients?

Artificial intelligence ChatGPT Nutrition Recommendation systems

Journal

Nutrition (Burbank, Los Angeles County, Calif.)
ISSN: 1873-1244
Titre abrégé: Nutrition
Pays: United States
ID NLM: 8802712

Informations de publication

Date de publication:
11 Nov 2023
Historique:
received: 21 07 2023
accepted: 30 10 2023
medline: 16 2 2024
pubmed: 16 2 2024
entrez: 15 2 2024
Statut: aheadofprint

Résumé

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.

Identifiants

pubmed: 38359704
pii: S0899-9007(23)00319-2
doi: 10.1016/j.nut.2023.112291
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

112291

Informations de copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest None.

Auteurs

Ilias Papastratis (I)

The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece. Electronic address: papastrat@iti.gr.

Andreas Stergioulas (A)

The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece.

Dimitrios Konstantinidis (D)

The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece.

Petros Daras (P)

The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece.

Kosmas Dimitropoulos (K)

The Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Central Macedonia, Greece.

Classifications MeSH