JMIR Publications

ABSTRACT

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 Artificial Intelligence 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 an AX-driven framework that integrates multimodal data acquisition, aligns exposure and response in time-series formats, and enables centralized analytical processing. This approach supports dynamic interpretation of intervention effects, improves data reliability, and facilitates individualized outcome assessment. Closing the AX gap is essential for advancing human trials from fragmented data collection systems to integrated, insight-generating platforms that support precision-based decision-making in dietary and cosmetic applications.