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Enhancing Hospital Nutrition Assessment Through Artificial Intelligence: A Prospective Tray-Level Pilot Study.

Sofia Favaretto1,2, Honoria Ocagli1, Giorgia Shasivari3

  • 1Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac-Thoracic-Vascular Sciences and Public Health, University of Padova, 35121 Padua, Italy.

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Summary

This study explored artificial intelligence (AI) for monitoring hospital patient food intake. AI shows feasibility for nutritional assessment, but further research is needed to confirm its superiority over traditional methods.

Keywords:
artificial intelligencedisease-related malnutritionfood intake estimationfood wastehospitalized patientsimage-based dietary assessment

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Area of Science:

  • Clinical Nutrition
  • Artificial Intelligence in Healthcare
  • Hospital Food Services

Background:

  • Disease-related malnutrition impacts 30-50% of hospitalized patients, increasing adverse outcomes and costs.
  • Current dietary intake monitoring via nursing diaries is subjective and labor-intensive.
  • Artificial intelligence (AI) offers potential for accurate and efficient nutritional assessment.

Purpose of the Study:

  • To evaluate the feasibility of an AI system for estimating food intake in hospitalized adults.
  • To compare AI performance against gold-standard meal weighing and nurse-completed diaries.
  • To assess AI-based nutritional assessment in a real-world hospital setting.

Main Methods:

  • Prospective observational pilot study conducted in a general medicine unit.
  • Food intake assessed via manual weighing, nursing diaries, and AI image analysis at the tray level.
  • Analysis of 362 meals from 67 patients between June and August 2025.

Main Results:

  • Nursing diaries showed 60.8% concordance with weighed intake, often overestimating consumption.
  • The AI system achieved a mean absolute error of ~40g (~10% of average tray weight).
  • Overall food waste was 30.7%; AI uncertainty estimates require cautious interpretation.

Conclusions:

  • AI-based food intake monitoring is feasible in a hospital setting.
  • Findings are exploratory and based on tray-level analysis.
  • A systematic underestimation bias was noted; AI superiority over routine methods is not yet established.