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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Nov 14, 2023
Open Peer Review Period: Nov 14, 2023 - Jan 9, 2024
Date Accepted: Oct 8, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review

Zheng J, Wang J, Shen J, An R

Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review

J Med Internet Res 2024;26:e54557

DOI: 10.2196/54557

PMID: 39608003

PMCID: 11638690

Artificial Intelligence Applications to Measure Food and Nutrient Intakes: A Scoping Review

  • Jiakun Zheng; 
  • Junjie Wang; 
  • Jing Shen; 
  • Ruopeng An

ABSTRACT

Background:

Measuring food and nutrient intakes has historically been challenging, often relying on subjective recall or labor-intensive diaries. With the advent of artificial intelligence (AI), there exists potential for more precise and efficient dietary assessment.

Objective:

This scoping review aimed to synthesize existing literature on the efficacy, accuracy, and challenges of employing AI tools in assessing food and nutrient intakes, offering insights into their current advantages and areas of improvement.

Methods:

A systematic literature search was conducted in PubMed, Web of Science, Cochrane Library, and EBSCO databases.

Results:

Our search revealed 25 pertinent studies published between 2010 and 2023. The included studies showcased the utility of AI in dietary assessments across various contexts and populations. Measures of food intake ranged from visual recognition of food items to intricate nutrient analyses facilitated by advanced machine learning algorithms. Comparative results underscored the superiority of AI in certain aspects, such as real-time data collection and minimizing recall bias, over traditional methods.

Conclusions:

AI-based approaches also presented limitations, including challenges with diverse food items and potential biases in algorithms. The broader implications encompassed the potential of AI in population-level dietary assessment studies, precision nutrition, and disease management.


 Citation

Please cite as:

Zheng J, Wang J, Shen J, An R

Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review

J Med Internet Res 2024;26:e54557

DOI: 10.2196/54557

PMID: 39608003

PMCID: 11638690

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