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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Demographic and Physical Determinants of Unhealthy Food Consumption in Polish Long-Term Care Facilities.

Nutrients·2025
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Large Language Models for Real-World Nutrition Assessment: Structured Prompts, Multi-Model Validation and Expert

Aia Ase1, Jacek Borowicz2, Kamil Rakocy3

  • 1Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, 02-097 Warsaw, Poland.

Nutrients
|January 10, 2026
PubMed
Summary

Advanced artificial intelligence (AI) models show high accuracy in classifying Polish foods, aiding nutrition assessment. While AI offers efficiency, expert review remains crucial for reliable dietary data.

Keywords:
artificial intelligence in healthcareartificial intelligence in nutritiondietary managementdigital toolslarge language modelsnutrition estimation

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Area of Science:

  • Artificial Intelligence in Nutrition
  • Computational Linguistics
  • Dietary Assessment Methodologies

Background:

  • Traditional dietary assessment methods suffer from reporting bias and scalability issues.
  • Large language models (LLMs) show promise for automated food classification.
  • Limited validation exists for LLMs in complex, non-English languages like Polish.

Purpose of the Study:

  • To validate advanced LLMs for automated food classification in Polish.
  • To compare the performance of different LLMs and prompting strategies.
  • To assess LLM accuracy against human expert consensus in dietary classification.

Main Methods:

  • Analysis of 1992 food items from a Polish long-term care facility (LTCF) cohort.
  • Utilized three LLMs (Claude Opus 4.5, Gemini 3 pro, GPT-5.1-chat-latest).
  • Employed two prompting strategies: structured double-step (NOVA, WHO criteria) and simplified single-step.
  • Compared LLM classifications against consensus judgments from two human experts.

Main Results:

  • All LLMs demonstrated high agreement with human experts (90.3-94.2%).
  • Statistically significant differences observed in pairwise LLM comparisons (p < 0.001).
  • Structured prompts yielded high recall for unhealthy items but lower specificity; simplified prompts offered better overall accuracy and balanced profiles.

Conclusions:

  • Advanced LLMs achieve near-expert accuracy for Polish dietary classification, improving workflow efficiency.
  • Expert oversight is essential for validating AI-driven nutrition assessments.
  • Multi-model consensus and language-specific validation enhance AI reliability in nutrition.