Digital Health Monitoring with AI-Based Physiological Signal Interpretation in Real-World Environments

Abstract

The integration of artificial intelligence into digital health monitoring has produced a major shift from episodic clinical measurement to continuous physiological observation across everyday environments. Wearable devices, ambient sensors, and mobile computing platforms now generate dense streams of cardiovascular, respiratory, thermoregulatory, electrodermal, and motion-related signals, creating both opportunities and structural challenges for health systems. This paper examines digital health monitoring with AI-based physiological signal interpretation from a systems perspective, emphasizing architecture, deployment, governance, robustness, fairness, and long-term sustainability. Rather than focusing narrowly on model performance, the discussion addresses how real-world variability, sensor heterogeneity, data quality degradation, privacy constraints, and regulatory requirements reshape the design of monitoring infrastructures. The paper analyzes the layered organization of data acquisition, signal processing, machine learning inference, and clinical decision support. It further considers how adaptive algorithms must operate under nonstationary conditions, uncertain labeling, missing data, and diverse population contexts. Governance mechanisms, algorithmic accountability, and health equity are treated as central system requirements rather than peripheral ethical add-ons. Through cross-domain comparisons and forward-looking analysis, the paper argues that sustainable digital health monitoring depends on architectures that balance real-time performance with explainability, interoperability, privacy preservation, and resilience. The conclusion identifies key priorities for future research and policy, including harmonized evaluation frameworks, federated learning infrastructures, and stronger post-deployment surveillance of AI-based physiological monitoring systems.