Multimodal Artificial Intelligence for Early Detection of Cardiometabolic Risk Using Wearable Data
Keywords:
wearable sensors; multimodal artificial intelligence; cardiometabolic risk; digital biomarkers; federated learning; algorithmic fairnessAbstract
Cardiometabolic risk arises from interacting physiological, behavioral, and environmental processes that frequently precede overt cardiovascular disease and type 2 diabetes. Wearable devices now generate continuous, multimodal data streams that can capture early perturbations in cardiovascular, metabolic, respiratory, and activity-related function. Artificial intelligence offers a promising pathway for transforming these heterogeneous data into clinically meaningful early warning signals. However, translating multimodal learning into reliable cardiometabolic risk detection requires addressing deeper system-level challenges beyond model accuracy. This paper presents a systems-oriented examination of multimodal artificial intelligence for early detection of cardiometabolic risk using wearable data. It analyzes the structural characteristics of wearable data streams, the architectural trade-offs involved in multimodal fusion, and the infrastructure requirements for longitudinal data integration. The discussion further considers robustness under real-world deployment, algorithmic fairness, privacy preservation, regulatory governance, and long-term system sustainability. Throughout, the paper argues that the central scientific and practical challenge is not solely the design of predictive models, but the construction of an integrated sociotechnical infrastructure in which multimodal models can operate safely, equitably, and sustainably. The analysis draws on recent advances in wearable sensing, deep learning, federated learning, clinical decision support, and algorithmic ethics to identify structural conditions under which early cardiometabolic risk detection may become a dependable component of preventive health systems.
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