Integration of wearable technology and artificial intelligence in digital health for remote patient care

Abstract

Wearable technology has transformed patient care in the digital health era, offering real-time health monitoring and personalized interventions. However, its full potential is hindered by several challenges, such as data privacy breaches due to insecure transmission of sensitive vitals, poor integration with electronic health records (EHRs), and limited adoption among older populations with low digital literacy. Additionally, the vast volume of real-time health data from wearables leads to data overload and usability issues in clinical settings. To address these issues, this study identifies and categorizes key barriers to wearable technology adoption and proposes targeted AI-driven solutions. We evaluate methods such as federated learning for privacy, deep learning for noise filtering in EEG data, and real-time anomaly detection to support clinical decision-making. The outcomes show improved data accuracy, reduced workload for healthcare providers, and increased patient engagement and trust. Moreover, the integration of blockchain with AI is explored to support secure, interoperable, and decentralized healthcare systems. Our work provides a structured, literature-based roadmap that links specific AI methods to clearly defined clinical challenges in remote patient care. This contribution supports developers, clinicians, and policymakers by offering practical insight into scalable and ethically grounded AI-wearable integration. Continued collaboration between technologists, healthcare professionals, and policymakers is essential to ensure scalable, equitable, and secure digital health implementations.

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