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Nutrition, Data Science and Artificial Intelligence

The Nutrition, Data Science, and Artificial Intelligence special collection aims to explore the intersection of nutrition science, data analytics, and AI. We invite submissions that leverage advanced computational techniques to analyse large-scale dietary data, predict nutritional outcomes, and develop innovative solutions for personalised dietary recommendations and dietary pattern analysis.
 

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Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review

Details
Category: Nutrition, Data Science and Artificial Intelligence
Hits: 88
ID: 103
  • Study Status: Published
  • Study Type: Review
  • Study Location: Greece

Study Title
Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review

Principal Investigator
Dimitris Tsolakidis, Lazaros P. Gymnopoulos, Kosmas Dimitropoulos

Affiliation
Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Greece

Start Date
Not specified

End Date
Not specified

Study Objective
To review the state of the art in data-driven technologies for personalized nutrition, including AI and ML, examining their application in developing personalized diet plans through user data collection.

Short Abstract
This systematic review investigates AI and machine learning (ML) techniques in personalized nutrition (PN), with a focus on recommender systems and data collection technologies. It highlights their potential to provide individualized dietary recommendations and discusses challenges, such as data privacy, technology integration, and the need for accurate datasets. The study reviews 67 works, emphasizing the role of recommender systems and emerging technologies like wearables in PN.

Study Design
Systematic literature review following the PRISMA guidelines

Population
Global applications of AI and ML in personalized nutrition

Sample Size
67 studies

Inclusion Criteria
Studies published from 2021 to 2024, focusing on AI and ML applications in personalized nutrition, including recommendation systems and data collection technologies

Exclusion Criteria
Studies not focused on AI or ML, those outside the scope of personalized nutrition, and those not peer-reviewed

Intervention/Exposure
AI and ML techniques for personalized nutrition recommendations, including recommender systems, wearables, and data-driven approaches

Outcome Measures
Development of personalized nutrition recommendations based on individual data, effectiveness of recommendation systems, and data collection methodologies

Funding Source
Not specified

Collaborating Institutions
Centre for Research and Technology Hellas (CERTH)

Ethics Approval
Not specified

Publication Status
Published in Informatics (2024)

Keywords
Personalized nutrition, artificial intelligence, machine learning, recommender systems, health data, wearable sensors

Data Collection Methods
Systematic literature review of studies from Scopus, ScienceDirect, and IEEE Xplore

Primary Data Availability
Not applicable

Contact Information
Dimitris Tsolakidis: This email address is being protected from spambots. You need JavaScript enabled to view it.