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Purpose of Review <p>To conduct an overview of the potentialities of artificial intelligence in precision nutrition.</p> Recent Findings <p>A keyword co-occurrence analysis of 654 studies on artificial intelligence (AI) and precision nutrition (PN) highlighted the potential of AI techniques like Random Forest and Gradient Boosting in improving personalized dietary recommendations. These methods address gastrointestinal symptoms, weight management, and cardiometabolic markers, especially when incorporating data on gut microbiota. Despite its promise, challenges like data privacy, bias, and ethical concerns remain. AI must complement healthcare professionals, necessitating clear guidelines, robust governance, and ongoing research to ensure safe and effective applications.</p> Summary <p>The integration of AI into PN enables highly personalized dietary recommendations by accounting for metabolic variability, genetics, and microbiome data. AI-driven strategies show potential in managing conditions like obesity and diabetes through accurate predictions of individual dietary responses. However, ethical, regulatory, and practical challenges must be addressed to ensure safe, equitable, and effective application of AI in nutrition.</p>

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Assessing the Links Between Artificial Intelligence and Precision Nutrition

  • 期刊论文

  • 发表日期：

    2025年3月15日

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  • Danton Diego Ferreira,
  • Lívia Garcia Ferreira,
  • Katiúcia Alves Amorim,
  • Deyvis Cabrini Teixeira Delfino,
  • Ana Cláudia Barbosa Honório Ferreira,
  • Leandra Passarelli Castro e Souza

摘要

Purpose of Review

To conduct an overview of the potentialities of artificial intelligence in precision nutrition.

Recent Findings

A keyword co-occurrence analysis of 654 studies on artificial intelligence (AI) and precision nutrition (PN) highlighted the potential of AI techniques like Random Forest and Gradient Boosting in improving personalized dietary recommendations. These methods address gastrointestinal symptoms, weight management, and cardiometabolic markers, especially when incorporating data on gut microbiota. Despite its promise, challenges like data privacy, bias, and ethical concerns remain. AI must complement healthcare professionals, necessitating clear guidelines, robust governance, and ongoing research to ensure safe and effective applications.

Summary

The integration of AI into PN enables highly personalized dietary recommendations by accounting for metabolic variability, genetics, and microbiome data. AI-driven strategies show potential in managing conditions like obesity and diabetes through accurate predictions of individual dietary responses. However, ethical, regulatory, and practical challenges must be addressed to ensure safe, equitable, and effective application of AI in nutrition.