AI-Driven Wearable Bioelectronics in Digital Healthcare

• Published in: Biosensors (Basel) Vol. 15; no. 7; p. 410
• Main Authors: Huang, Guangqi, Chen, Xiaofeng, Liao, Caizhi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.06.2025; MDPI
• Subjects: Algorithms; Analysis; Artificial Intelligence; artificial intelligence (AI); Biological markers; Biomarkers; Biosensing Techniques; Biosensors; Blood sugar monitoring; Business metrics; Chronic diseases; Chronic illnesses; Collaboration; Customization; Data transmission; Delivery of Health Care; Diabetes; digital healthcare; Disease detection; disease diagnosis; Disease management; Empowerment; Energy efficiency; Glucose; Health aspects; Health care; healthcare monitoring; Humans; Innovations; Monitoring, Physiologic; Nanostructured materials; Neural networks; Physiology; Power; Prevention; Real time; Remote monitoring; Review; Risk factors; Sensors; Technological change; Telemedicine; wearable bioelectronics; Wearable computers; Wearable Electronic Devices; Wearable technology; China; disease diagnosis; digital healthcare; healthcare monitoring; wearable bioelectronics; artificial intelligence (AI)
• ISSN: 2079-6374; 2079-6374
• DOI: 10.3390/bios15070410

The integration of artificial intelligence (AI) with wearable bioelectronics is revolutionizing digital healthcare by enabling proactive, personalized, and data-driven medical solutions. These advanced devices, equipped with multimodal sensors and AI-powered analytics, facilitate real-time monitoring of physiological and biochemical parameters—such as cardiac activity, glucose levels, and biomarkers—allowing for early disease detection, chronic condition management, and precision therapeutics. By shifting healthcare from reactive to preventive paradigms, AI-driven wearables address critical challenges, including rising chronic disease burdens, aging populations, and healthcare accessibility gaps. However, their widespread adoption faces technical, ethical, and regulatory hurdles, such as data interoperability, privacy concerns, algorithmic bias, and the need for robust clinical validation. This review comprehensively examines the current state of AI-enhanced wearable bioelectronics, covering (1) foundational technologies in sensor design, AI algorithms, and energy-efficient hardware; (2) applications in continuous health monitoring, diagnostics, and personalized interventions; (3) key challenges in scalability, security, and regulatory compliance; and (4) future directions involving 5G, the IoT, and global standardization efforts. We highlight how these technologies could democratize healthcare through remote patient monitoring and resource optimization while emphasizing the imperative of interdisciplinary collaboration to ensure equitable, secure, and clinically impactful deployment. By synthesizing advancements and critical gaps, this review aims to guide researchers, clinicians, and policymakers toward responsible innovation in the next generation of digital healthcare.