Explainable Artificial Intelligence for Continuous Cardiovascular Monitoring from Wearable Devices
Keywords:
explainable artificial intelligence; wearable devices; cardiovascular monitoring; system architecture; regulatory governance; fairness; robustnessAbstract
Continuous cardiovascular monitoring through wearable devices has created new opportunities for early detection, longitudinal risk stratification, and personalized intervention outside clinical settings. At the same time, the increasing use of opaque machine learning models in processing photoplethysmography, electrocardiography, and accelerometry signals raises fundamental challenges for clinical trust, safety, and accountability. This paper presents a system-level examination of explainable artificial intelligence for continuous cardiovascular monitoring. Rather than treating explainability as a purely algorithmic property, the discussion situates it within a layered architecture that includes sensing, edge processing, inference, explanation generation, and clinical presentation. The paper analyzes structural trade-offs between on-device and cloud-based computation, post hoc and intrinsically interpretable models, local attributions and global explanations, and responsive latency against explanatory depth. It further examines robustness, uncertainty quantification, fairness, regulatory governance, privacy, and long-term sustainability as interdependent system properties. A central argument is that explainability must be designed as an infrastructural capability rather than retrofitted as an interface feature. The article draws on cross-domain comparisons from clinical machine learning, responsible artificial intelligence, and large-scale health infrastructure to outline forward-looking requirements for trustworthy deployment. Policy implications include the need for prospective clinical evaluation, transparent model documentation, federated data governance, and sustainability-aware architecture decisions. The objective is to provide a scholarly and practical framework for building cardiovascular monitoring systems whose explanations are clinically meaningful, technically grounded, and institutionally enforceable.
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