Data-Rich But Integration-Poor Human Trials: Conceptual Framework for Closing the AI Transformation Gap in Dietary...

Human trials in dietary and cosmetic domains generate large volumes of multimodal data but remain limited in their ability to produce integrated and actionable insights. Data such as intake records, physiological biomarkers, imaging outputs, and user-reported outcomes are typically collected through disconnected systems and analyzed independently, restricting the ability to establish time-resolved relationships between exposure and response. In this viewpoint, we define this limitation as the AI transformation (AX) gap, representing a structural disconnect between data generation and actionable knowledge in human trials. Drawing on evidence from real-world industry demand, we highlight how this gap is consistently observed across dietary intervention trials, cosmetic human trials, and underlying data systems. We propose a conceptual AX-driven framework that integrates multimodal data acquisition, continuously aligns intervention exposure with corresponding physiological, phenotypic, and participant-reported responses across time, and supports centralized analytical processing. The framework is intended to provide a unified perspective for transforming fragmented human-trial data into integrated, interpretable, and actionable knowledge. Although the proposed framework has not yet been empirically validated, it offers a conceptual foundation for future development and evaluation of integrated data platforms in dietary intervention and cosmetic human trials. Closing the AX gap may facilitate the transition from fragmented data collection toward human trials in which multimodal exposure and response data are continuously linked across time to support more integrated and individualized interpretation in research and product development.