Artificial Intelligence-Driven Wearable Sensors for Cardiovascular Health Monitoring
Clinical Snapshot
PICO Framework
| P — Population | Patients requiring cardiovascular health monitoring |
| I — Intervention | AI-driven wearable sensors for cardiovascular monitoring |
| C — Comparator | Traditional cardiovascular monitoring methods or non-AI wearable devices |
| O — Outcomes | Cardiovascular health monitoring effectiveness, early detection capabilities, disease management outcomes |
Bottom Line
This narrative review provides a broad overview of AI-driven wearable sensors for cardiovascular monitoring, covering sensor design, calibration, signal processing, pattern recognition, and disease management applications. While the technology shows promise for continuous, non-invasive cardiovascular monitoring, the review lacks systematic methodology and quantitative evidence synthesis. The authors identify key challenges including sensor design inefficiencies, calibration model degradation, and complex signal processing under real-world conditions. Although AI integration may address these limitations, the evidence presented is insufficient to support immediate practice change. Clinicians should await systematic reviews and randomised controlled trials demonstrating clinical effectiveness, safety, and cost-effectiveness before widespread adoption. The technology may complement but not replace established cardiovascular monitoring methods in Australian healthcare settings.
Key Findings
Effect Size: Not quantified - descriptive review
Primary Outcome: Overview of AI integration benefits in wearable cardiovascular monitoring
Nnt Or Sensitivity: Not applicable - no quantitative analysis performed
Clinical Application
Technology still emerging with implementation challenges including calibration accuracy, signal processing complexity, and real-world validation needs Relevant to Australian digital health initiatives and TGA medical device regulations, but requires local validation studies and integration with existing cardiovascular care pathways Patients requiring continuous cardiovascular monitoring, particularly those with known cardiovascular disease or risk factors
Abstract
Wearable sensing technologies hold great promise for continuous and non-invasive monitoring of cardiovascular health, offering new avenues for early detection and effective management of cardiovascular diseases. Despite these advances, several critical challenges remain, including inefficiencies in sensor design, the degradation of calibration model accuracy over time, and difficulties in processing complex physiological signals under real-world conditions. The integration of artificial intelligence (AI) into wearable cardiovascular monitoring systems presents transformative solutions to these limitations by improving system performance, adaptability, and clinical applicability. In this review, we provide an overview of current wearable technologies for cardiovascular monitoring, including their mechanisms, characteristics, and limitations. An in-depth discussion is subsequently presented on AI-driven wearable cardiovascular monitoring, encompassing five critical facets: sensor design, sensor calibration, signal processing, pattern recognition, and disease management. Furthermore, we analyze the challenges associated with the widespread use of AI-based wearable systems and provide insights into potential strategies for addressing these challenges. This review is expected to serve as a roadmap for future research and development in the rapidly evolving field of intelligent cardiovascular health monitoring.
References
- 1.Deng, H., Ma, B., Chen, G., Chi, J., Tong, J., Yu, F., Lu, B., Chen, Y., Zhao, C., Zhang, C., & Liu, H. (2026). Artificial Intelligence-Driven Wearable Sensors for Cardiovascular Health Monitoring. ACS Sensors. https://doi.org/10.1021/acssensors.5c04840
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