AI-ECG for wearable monitoring: From arrhythmia diagnosis to early warning and multi-disease prediction
*Correspondence to:
Zhiyuan Li, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
E-mail: lzy2030@sjtu.edu.cn
BME Horiz. 2026;4:202619. 10.70401/bmeh.2026.0029
Received: April 01, 2026Accepted: June 08, 2026Published: June 08, 2026
This manuscript is made available in its unedited form to allow early access to the
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Abstract
Wearable electrocardiography (ECG) is shifting cardiovascular monitoring from episodic in-hospital testing to continuous out-of-hospital assessment. Artificial intelligence-enabled ECG (AI-ECG) provides a new pathway for extracting clinically useful information from out-of-hospital wearable recordings. This review is organized around the wearable AI-ECG monitoring pipeline, summarizing key advances in model development, clinical application, and real-world validation, while emphasizing the special requirements that wearable scenarios impose on algorithm design and clinical translation. At present, arrhythmia screening remains the most mature applications of AI-ECG, with deep learning models achieving cardiologist-level or clinician-comparable performance in several well-defined tasks. With the growing availability of long-term continuous recordings, research is further extending from post-event recognition to pre-event warning, particularly for high-risk events such as acute atrial fibrillation and malignant ventricular arrhythmias. Related methods are evolving from traditional feature-based machine learning toward deep learning and foundation models that can exploit waveform morphology, rhythm dynamics, and long-range temporal information. Beyond rhythm disorders, AI-ECG is also being explored for structural cardiac abnormalities, metabolic disorders, and broader systemic risk prediction, suggesting a potential role for ECG as a digital biomarker platform. However, several barriers continue to limit clinical translation, including limited cross-device and cross-population generalizability, insufficient interpretability, and the lack of prospective real-world validation. Future progress will likely depend on standardized data systems, artifact-aware modeling, cross-device validation, foundation models, longitudinal risk modeling, and intelligent systems designed for clinical workflows. Overall, wearable AI-ECG is evolving from passive abnormality detection toward continuous, proactive, and personalized health risk assessment.
Keywords
Wearable ECG monitoring, AI-ECG, arrhythmia diagnosis, early warning, deep learning
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Li Z, Tian Y, Jin Y, Liu C. AI-ECG for wearable monitoring: From arrhythmia diagnosis to early warning and multi-disease prediction. BME Horiz. 2026;4:202619. https://doi.org/10.70401/bmeh.2026.0029
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Li Z, Tian Y, Jin Y, Liu C. AI-ECG for wearable monitoring: From arrhythmia diagnosis to early warning and multi-disease prediction. BME Horiz. 2026;4:202619. https://doi.org/10.70401/bmeh.2026.0029
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