This Perspective synthesizes current AI/ML methodologies in PN, elucidates their interplay with the distinctive features of multi-omic and nutritional data, such as being compositional, episodic, context-dependent, and error-prone, and delineates nutrition-specific best practices for achieving robust, interpretable, and clinically actionable AI integration in research and practice.
Abstract
A key feature of the Precision Nutrition and Health approach is the ability to tailor interventions to individual variability using multimodal data from large-scale biobanks and cohorts. Artificial intelligence (AI) and machine learning (ML) models offer new potential to model complex data but remain constrained by challenges related to data quality, interpretability, validation, and causal inference. This Perspective synthesizes current AI/ML methodologies in PN, elucidates their interplay with the distinctive features of multi-omic and nutritional data, such as being compositional, episodic, context-dependent, and error-prone, and delineates nutrition-specific best practices for achieving robust, interpretable, and clinically actionable AI integration in research and practice. In this Perspective, the authors highlight critical challenges, knowledge gaps, and opportunities for robust, equitable, rigorous, reproducible, and actionable artificial intelligence integration in research and practice, and provide a roadmap and checklist for enabling the use of artificial intelligence in precision nutrition.
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.
Xinru Wu, Jianghua Feng· Journal of Food Science· 0 citations
Nutrition is one of the few modifiable risk factors for health status. The diet-disease relationship, however, is highly complex, with many endogenous and exogenous factors contributing to risk and with variation in responses to diet between individuals. The 2020-30 National Institutes of Health (NIH) Strategic Plan for Nutrition Research states that precision nutrition is a unifying and holistic approach to developing comprehensive and dynamic nutritional recommendations relevant to both individual and population health. Recognizing the growing importance of advanced computational approaches in nutrition research, in 2022 the NIH Office of Nutrition Research and participating NIH Institutes and Centers published a Funding Opportunity Announcement for training programs in AI for Precision Nutrition (AIPrN) aiming to develop a new generation of the scientific workforce, literate in both nutritional and computer sciences, that leverages the growing data resources available for nutrition research to tackle complex and emerging health-disease associations. In this paper, we describe the approaches developed by the first four institutional awardees of the T32 AIPrN training grant, and we highlight applied strategies for interdisciplinary training, data integration, and methodological innovation as the research community prepares to analyze and interpret data generated by major initiatives such as the NIH-funded Nutrition for Precision Health Initiative.
Saurabh Mehta, S. Huey, Paraskevi Massara et al.· Journal of NutriLife· 0 citations
This survey explores how machine learning and artificial intelligence (AI) can be integrated with mechanistic models to create more accurate, dynamic, predictive, and personalized representations of biological systems, commonly referred to as digital twins (DTs). Mechanistic models, such as pathway-based Boolean or differential equation frameworks, provide interpretable insights into biological processes; however, calibrating these models to represent individual variability across large, heterogeneous cohorts remains a significant challenge, as their physically constrained structures often lack the flexibility to capture complex, non-mechanistic nuances in patient data. Focusing on elderly cancer patients–a vulnerable population underrepresented in clinical research–we discuss how hybrid DTs can bridge the gap between interpretable mechanistic frameworks and flexible, predictive AI approaches, enabling continuous monitoring, risk stratification, and adaptive treatment planning. To illustrate these principles, we present a proof-of-concept case study involving a synthetic breast cancer dataset in which comprehensive geriatric assessment, clinical tests, and quality of life measures inform dosing decisions for older patients via a Markov Decision Process. By combining a synthesis of current literature with the application of a sequential decision-making framework optimized using longitudinal data, this work provides a foundational understanding for researchers and clinicians interested in leveraging DTs to improve personalization and outcomes in geriatric oncology.
Panagiotis Karampelesis, S. Denazis, O. Koufopavlou et al.· Frontiers in Artificial Inte...· 0 citations
A framework through XAI to incorporate various health data sources such as electronic health records, medical imaging, laboratory reports, and wearable sensor information, which can be integrated in the context of achieving higher predictive performance in disease prediction and treatment stratification, as well as decision-making transparency.
M. Aparna, S. Lahane, Dr. Bharti A. Dixit· Journal of Intelligent Decis...· 0 citations
Artificial intelligence (AI) is rapidly transforming healthcare, with growing impact on maternal and child health (MCH) through advances in machine learning, deep learning, computer vision, generative models, and conversational systems. This article provides a comprehensive synthesis of current AI applications in MCH, structured across six key domains: predictive modeling, image analysis, deep learning and interpretability, generative and multi-omics approaches, conversational AI, and environmental and lifestyle analytics. Drawing on recent literature and the translational experience of the Spanish RICORS-SAMID network, we analyze how these technologies are being integrated into clinical, preventive, and assistive workflows. Across domains, AI demonstrates strong potential for early risk prediction (e.g., preeclampsia, fetal growth restriction, neonatal outcomes), automated image interpretation, biomarker discovery, and personalized decision support. However, despite promising performance metrics, most systems remain at the proof-of-concept stage, with limited external validation, scarce prospective evaluation, and incomplete integration into real-world clinical pathways. Key translational gaps include data heterogeneity, lack of interoperability, insufficient explainability, and challenges related to bias, fairness, and regulatory compliance. We argue that the next phase of AI in MCH must shift from static prediction toward longitudinal, mechanism-aware, and clinically actionable systems, supported by robust validation and multidisciplinary collaboration. Particular emphasis is placed on equity, as the benefits of AI must extend to low-resource settings where maternal and neonatal morbidity remains highest. By bridging technical innovation with clinical implementation, coordinated research networks such as RICORS can play a critical role in accelerating the safe, effective, and equitable deployment of AI in maternal and child healthcare.
Paula Domínguez Del Olmo, Juan D Arévalo, C. Villalaín et al.· Women's Health· 0 citations
Overall, AI offers substantial opportunities to improve the personalization, efficiency, and scalability of clinical nutrition practice, but its safe and effective implementation will require continued validation, careful oversight, and integration with clinical expertise.
K. Mauldin, Anthony D. Pham, Sneha Dodaballapur et al.· Nutrients· 0 citations
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