Functional Food Science in the era of artificial intelligence: The role of domain authority, structured validation, and responsible translation

Abstract

The rapid expansion of artificial intelligence (AI) in nutrition and health research offers a powerful analytical tool; however, it also raises concerns when applied without a structured scientific framework or domain authority. Functional Food Science, as established and advanced by the Functional Food Center (FFC), represents a distinct discipline focused on the development of functional food products (FFP) with validated health benefits through bioactive compounds, measurable biomarkers, mechanistic pathways, clinical confirmation, and long-term population validation.

As AI’s influence expands, it should be utilized as a supportive analytical tool, rather than a replacement for researchers. Here, we examine the appropriate role of AI in functional food science and propose a structured model that integrates AI as an analysis tool within the established FFC framework, ensuring that decision-making remains in the researchers’ hands. This article examines the appropriate role of AI in functional food science and proposes a structured model in which AI serves as a supportive analytical tool within the established FFC framework, rather than as an independent decision-making system.

This article requires us to revisit the scientific foundations of functional food science. By contrasting the FFC development paradigm with the Japanese functional food model, which prioritizes pre-market evaluation over post-market epidemiological validation, we propose that AI’s role best aligns in post-market surveillance. In this role, AI can synthesize real-world data from electronic health records, wearable technologies, and population cohorts to confirm sustained efficacy, detect safety signals, and refine functional food formulations. When integrated into a closed-loop pipeline that extends beyond market release, AI improves evidence generation while maintaining mechanistic understanding, clinical rigor, and scientific accountability.

Responsible application of AI in functional food science requires domain expertise, structured validation, and long-term population evidence. Embedded within the FFC framework, AI has the potential to strengthen scientific rigor, improve translational accuracy, and support scalable health outcomes for global populations.

Novelty of the Study: This article introduces a conceptual framework positioning AI as an enabling technology within functional food science, with particular emphasis on AI-driven post-market epidemiological validation—a step historically underdeveloped in functional food systems. By explicitly integrating AI into the FFC’s structured development model, this work advances functional food science from a product-centered approach to a continuously validated health discipline.

Keywords: Functional food science; artificial intelligence; bioactive compounds; post-market surveillance; epidemiological validation; Functional Food Center; health biomarkers