# Generative AI in Precision Nutrition: A Review of Current Developments and Future Directions

## TL;DR

This paper reviews how generative AI is being used in precision nutrition, highlighting its potential and current limitations in personalizing dietary advice.

## Contribution

The paper systematically reviews the current state of generative AI applications in precision nutrition, emphasizing gaps in biological integration and evaluation practices.

## Key findings

- Most studies use large language models for personalized dietary recommendations based on user preferences.
- Fewer than half of the studies incorporate biological or metabolic data into their models.
- Evaluation methods often rely on synthetic data, raising concerns about reliability in clinical settings.

## Abstract

Background: Precision nutrition (PN) aims to personalize dietary guidance by accounting for inter-individual variability across biological, metabolic, lifestyle, and environmental factors influencing nutritional needs and health outcomes. While traditional Artificial Intelligence (AI) has advanced nutritional research through systems like automated dietary assessment, these models often operate rigidly. Generative AI (GenAI) introduces the capacity for adaptive interventions for enhanced PN. However, the scope and maturity of its applications remain insufficiently characterized. Objective: This review examined original works applying GenAI in PN, focusing on application, methodology, and limitations. Methods: A systematic search was conducted in PubMed, ACM Digital Library, and Scopus. Inclusion criteria focused on original works deploying GenAI models in PN contexts. Included works were further formally assessed based on data used, validation, transparency, bias, and security and privacy. Results: 21 eligible studies were identified, all published after 2024. The literature indicated a surge in large language model-based systems for personalized dietary recommendations, followed by applications in data foundation building and food effect understanding. A recurrent limitation was questionable evaluation on synthetic data and hallucinations, necessitating a human-expert-in-the-loop, especially in high-stakes clinical settings. Additionally, only 4 of 21 reviewed studies incorporated biological content or biological inputs, and fewer approached biologically grounded PN within implemented personalization workflows using metabolic and/or genomic variables. Conclusions: Although GenAI research in PN is expanding rapidly, most applications remain personalized at a user-preference level rather than including biological determinants. The need for standardized reporting, stronger genome-informed modeling, and consistent human-in-the-loop validation protocols is further highlighted to advance towards holistic PN.