Artificial Intelligence in Food-Nutrition-Health Research: From Multimodal Data Integration to Precision Intervention

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported — no quantitative synthesis performed

• Primary Outcome: Narrative synthesis of AI applications across food component analysis, nutrition-disease association modelling, pathogen identification, and personalised dietary intervention systems

• Nnt Or Sensitivity: Not reported — no diagnostic accuracy metrics, NNT, or hazard ratios are synthesised; AI model performance metrics are described qualitatively across application domains without aggregation

• Confidence Interval: Not reported

Clinical Application

Clinical implementation of AI-driven precision nutrition remains limited by the barriers the review itself identifies: lack of model generalisation across diverse populations, algorithmic opacity reducing clinician trust, data privacy constraints, and absence of standardised multi-omics integration protocols. Wearable device integration and federated learning approaches are promising but not yet validated for routine clinical use. In the Australian context, AI-assisted dietary intervention tools are not currently listed on the PBS or formally endorsed by the TGA for therapeutic use. The RACGP does not yet have specific guidelines for AI-guided nutrition prescribing. The Australian Digital Health Agency's national digital health strategy and the My Health Record infrastructure could theoretically support federated learning approaches described in this review, but regulatory and privacy frameworks (Privacy Act 1988, Australian Privacy Principles) would require careful navigation. Dietitians Australia and the Dietetic Association would need to be engaged for any clinical translation pathway. The review's findings are relevant to Australian researchers and policymakers planning investment in precision nutrition infrastructure but do not yet support changes to clinical practice. Potentially applicable to populations targeted for precision nutrition interventions, chronic disease prevention programmes, and food safety monitoring. Most directly relevant to research and public health contexts rather than individual clinical encounters at this stage of evidence development.

Abstract

Artificial intelligence (AI) is transforming food-nutrition-health research by enabling pattern recognition in complex, high-dimensional datasets that traditional hypothesis-driven approaches cannot address. This review systematically synthesizes research progress of AI across the food-nutrition-health continuum from 2020 to 2025. By examining 181 systematic reviews through PRISMA-guided selection, we provide a comprehensive overview and prospects across four dimensions: technical foundation, application scenarios, existing challenges, and future prospects. We propose a tripartite framework comprising (1) a data layer enabling multisource fusion of food composition, health monitoring, and individual characteristic data; (2) a technological layer of nondestructive testing (spectroscopy, nuclear magnetic resonance [NMR], imaging); and (3) an algorithmic layer progressing from machine learning to deep learning architecture. Key applications include food component analysis and safety detection; nutrition-disease association modeling; pathogen identification; and personalized dietary intervention systems. Despite rapid progress, critical challenges persist, insufficient model generalization across populations, algorithmic opacity limiting clinical trust, data privacy vulnerabilities, and lack of standardized multi-omics integration protocols. Future directions emphasize multimodal fusion models, explainable artificial intelligence (XAI), federated learning for privacy-preserving collaboration, gene-guided precision nutrition, and development of intelligent wearable devices and functional food. This review provides a roadmap for transitioning from population-averaged guidelines to dynamic, individualized health optimization through AI-enabled food system.

References

1. 1.Wu, X., & Feng, J. (2026). Artificial intelligence in food-nutrition-health research: From multimodal data integration to precision intervention. Journal of Food Science. Advance online publication. https://doi.org/10.1111/1750-3841.71334