AI-Driven Wearable Bioelectronics in Digital Healthcare

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 monitorin...

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Published inBiosensors (Basel) Vol. 15; no. 7; p. 410
Main Authors Huang, Guangqi, Chen, Xiaofeng, Liao, Caizhi
Format Journal Article
LanguageEnglish
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)
Online AccessGet full text
ISSN2079-6374
2079-6374
DOI10.3390/bios15070410

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Abstract 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.
AbstractList 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.
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.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.
Audience Academic
Author Liao, Caizhi
Huang, Guangqi
Chen, Xiaofeng
AuthorAffiliation 3 Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA; x.chen@utexas.edu
1 Department of Bioelectronics, Faculty of Biomedical Engineering, Shenzhen University of Advanced Technology, Shenzhen 518055, China; gqhuang.pku@outlook.com
2 Division of Electrical Engineering, Department of Engineering, Cambridge University, Cambridge CB2 1TN, UK
AuthorAffiliation_xml – name: 2 Division of Electrical Engineering, Department of Engineering, Cambridge University, Cambridge CB2 1TN, UK
– name: 3 Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA; x.chen@utexas.edu
– name: 1 Department of Bioelectronics, Faculty of Biomedical Engineering, Shenzhen University of Advanced Technology, Shenzhen 518055, China; gqhuang.pku@outlook.com
Author_xml – sequence: 1
  givenname: Guangqi
  surname: Huang
  fullname: Huang, Guangqi
– sequence: 2
  givenname: Xiaofeng
  orcidid: 0000-0002-5399-9781
  surname: Chen
  fullname: Chen, Xiaofeng
– sequence: 3
  givenname: Caizhi
  orcidid: 0000-0003-1271-3027
  surname: Liao
  fullname: Liao, Caizhi
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Issue 7
Keywords disease diagnosis
digital healthcare
healthcare monitoring
wearable bioelectronics
artificial intelligence (AI)
Language English
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SubjectTerms 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
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Title AI-Driven Wearable Bioelectronics in Digital Healthcare
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