AI-Driven Wearable Bioelectronics in Digital Healthcare

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.

Keywords: artificial intelligence (AI); digital healthcare; disease diagnosis; healthcare monitoring; wearable bioelectronics.

Figure 2

A timeline of key milestones in wearable sensor development (a), along with a summary of their core components (b). Reproduced with permission [60]. Copyright 2022, Springer Nature Limited.

Figure 3

Materials and structural design of a fully bioresorbable, wireless, and battery-free electrotherapy system. For i–v, photographs capturing the dissolution of an inner electrode structure with interconnects at various immersion times in Dulbecco’s phosphate-buffered saline (DPBS; pH 7.4) at 75 °C. Scale bar: 5 mm. Reproduced with permission [92]. Copyright 2023, Creative Commons Attribution License 4.0 (CC BY).

Figure 4

Design of stretchable graphene–hydrogel nanocomposites. Illustration of the structure of thin, stretchable nanocomposites enhanced with antibacterial and biocompatible PPH hydrogel, suitable for wearable and implantable bioelectronics. Reproduced with permission [103]. Copyright 2024, Springer Nature Limited.

Figure 5

Image and schematic illustration of the monolithically integrated in-textile wristband. Schematic of the textile wristband designed for wireless sweat K⁺ analysis. Photograph and schematic of the flexible patch (2.5 cm × 9 cm). Logical flow of the system design. Reproduced with permission [107]. Copyright 2023, Creative Commons Attribution License 4.0 (CC BY).

Figure 6

Schematic flow chart of self-powering smart wearable sensors. The process involves harvesting energy from various sources, converting it into electrical power, storing the energy, and using it to power wearable sensors. Energy Sources: PV (Photovoltaic); BFCs (Biofuel Cells); TEGs (Thermoelectric Generators); PENGs (Piezoelectric Nanogenerators); TENGs (Triboelectric Nanogenerators); RF (Radio Frequency); EMGs (Electromagnetic Generators). Reproduced with permission [131]. Copyright 2021, Creative Commons Attribution License 4.0 (CC BY).

Figure 9

Non-Invasive, Wireless Wearable Biosensor for Continuous Electrochemical Monitoring of Inflammation. Reproduced with permission [175]. Copyright 2023, Springer Nature Limited.

Figure 10

A wireless smart bandage for managing wound exudate, analyzing reactive species, and providing personalized wound assessment. Reproduced with permission [187]. Copyright 2025, American Association for the Advancement of Science.

Figure 1

AI-driven wearable bioelectronics and their broad applications in digital healthcare.

Figure 7

The broad applications of AI in healthcare, including disease treatment, accurate diagnosis, electronics health records, etc.

Figure 8

Integration of AI with edge computing for wearable bioelectronics devices.

Figure 11

Challenges for AI integrating with wearable bioelectronics.

Figure 12

Future directions for the development of AI-driven wearable bioelectronics.