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<p>Artificial intelligence (AI) is transforming dietary assessment, yet few tools have been clinically validated against physiological reference methods. This cross-sectional observational validation study conducted under free-living conditions evaluated the validity of SNAQ, an AI-powered image-based dietary assessment app, against doubly labelled water (DLW) in females with obesity. Twenty participants completed a 7-day protocol, including DLW-based measurement of total daily energy expenditure (TDEE) and estimation of total daily energy intake using SNAQ and 24-h dietary recall (24HR). Compared with DLW-derived TDEE (3004 ± 481 kcal/day), SNAQ underestimated energy intake by 25% (bias −817 kcal/day; limits of agreement −3707 to 2073 kcal/day), while 24HR underestimated intake by 50%. Individual-level agreement had negligible within-subject reliability (ICC = 0.00). Despite advanced AI architecture, SNAQ showed systematic group-level underestimation and poor individual-level agreement, underscoring the translational gap between algorithmic performance and clinical feasibility and the need for standardised clinical validation before implementation.</p>

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Limited validity of an AI-powered app for dietary assessment in females with obesity

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月17日

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  • Michele Serra,
  • Daniela Alceste,
  • Nicole Jucker,
  • Lotta Haupt,
  • Sebastian Elben,
  • Samuel Müller,
  • Paul J. M. Hulshof,
  • Harro A. J. Meijer,
  • Andreas Thalheimer,
  • Robert E. Steinert,
  • Philipp A. Gerber,
  • Alan C. Spector,
  • Daniel Gero,
  • Marco Bueter

摘要

Artificial intelligence (AI) is transforming dietary assessment, yet few tools have been clinically validated against physiological reference methods. This cross-sectional observational validation study conducted under free-living conditions evaluated the validity of SNAQ, an AI-powered image-based dietary assessment app, against doubly labelled water (DLW) in females with obesity. Twenty participants completed a 7-day protocol, including DLW-based measurement of total daily energy expenditure (TDEE) and estimation of total daily energy intake using SNAQ and 24-h dietary recall (24HR). Compared with DLW-derived TDEE (3004 ± 481 kcal/day), SNAQ underestimated energy intake by 25% (bias −817 kcal/day; limits of agreement −3707 to 2073 kcal/day), while 24HR underestimated intake by 50%. Individual-level agreement had negligible within-subject reliability (ICC = 0.00). Despite advanced AI architecture, SNAQ showed systematic group-level underestimation and poor individual-level agreement, underscoring the translational gap between algorithmic performance and clinical feasibility and the need for standardised clinical validation before implementation.