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Related Experiment Video

Updated: Jan 20, 2026

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Large Language Model-Based Text Recognition and Structured Data Extraction for Dietary Surveys.

Fangxu Guan1, Ruixue Niu2, Feifei Huang1

  • 1Key Laboratory of Public Nutrition and Health, National Health Commission of the People's Republic of China; National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.

China CDC Weekly
|January 19, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) improve dietary surveys by accurately processing audio recordings into structured data. This AI-driven approach enhances data integrity and consistency for nutrition research.

Keywords:
cohort studydietary surveylarge language model

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Area of Science:

  • Nutrition Science
  • Artificial Intelligence
  • Data Science

Background:

  • Traditional dietary surveys are labor-intensive and prone to inaccuracies.
  • Accurate nutritional data collection is crucial for public health research.
  • Advancements in large language models (LLMs) offer potential solutions for data collection challenges.

Purpose of the Study:

  • To evaluate the effectiveness of LLMs in improving the accuracy and efficiency of dietary surveys.
  • To assess the performance of LLM-based data extraction against manual methods.

Main Methods:

  • A 24-hour dietary recall protocol was employed with an intelligent recording pen capturing audio data.
  • Audio recordings were transcribed and processed using GLM-4 for prompt engineering and chain-of-thought reasoning.
  • LLM-generated structured data integrity and consistency were analyzed, with precision and F1 scores calculated.

Main Results:

  • LLM-based structured data achieved an overall integrity rate of 92.5% and 86% consistency with manual records.
  • The LLM demonstrated high accuracy in recognizing food ingredients and locations.
  • The model achieved 94% precision and an 89.7% F1 score on the dataset.

Conclusions:

  • LLM-powered text recognition and data extraction serve as valuable tools for enhancing dietary survey efficiency and accuracy.
  • Continued development of AI tools promises more precise and efficient data collection in nutrition research.