Cardiac arrhythmias are a growing global health concern, and the need for accessible, continuous monitoring has driven rapid advancements in wearable technologies. This review explores the evolution, capabilities, and clinical impact of modern wearables for arrhythmia detection, including smartwatches, smart rings, ECG patches, and smart textiles. In light of the recent surge in commercially available wearables across all categories, this review offers a detailed comparative analysis of leading devices, evaluating cost, regulatory approval, model specifications, and system compatibility. Smartwatches and patches, in particular, show a strong performance in atrial fibrillation detection, with patches outperforming Holter monitors in long-term monitoring and diagnostic yield. This review highlights a paradigm shift toward patient-initiated diagnostics but also discusses challenges such as false positives, regulatory gaps, and healthcare integration. Overall, wearable devices hold significant promise for reshaping arrhythmia management through early detection and remote monitoring.

Background and objectives: Atrial fibrillation (AF), the most prevalent sustained arrhythmia, poses a significant public health challenge due to its links with stroke, heart failure, and mortality. Hypertension, a primary modifiable cardiovascular risk factor, is a well-established risk factor for AF that facilitates structural and electrical changes in the atria, including dilation, fibrosis, and pressure overload. Material and Methods: we conducted a literature search regarding the shared mechanisms, risks and treatments of hypertension and atrial fibrillation. Results: The renin–angiotensin–aldosterone system plays a pivotal role in this remodelling and inflammation, increasing AF susceptibility. Uncontrolled hypertension complicates AF management, diminishing the effectiveness of mainstay treatments, including antiarrhythmic drugs, catheter ablation, and cardioversion. Effective blood pressure management, particularly with therapies targeting the renin–angiotensin–aldosterone system (RAAS), can lower the risk of new-onset AF and reduce the incidence of recurrent AF, enhancing the success of rhythm control strategies. These antihypertensive therapies mitigate myocardial hypertrophy and fibrosis and attenuate both atrial pressure strain and the inflammatory response, mitigating the substrates for AF. Conclusion: This review highlights the urgent need for integrated strategies that combine BP control, AF screening, and lifestyle modifications to minimise the burden of AF and its complications. Future research should investigate the specific mechanisms of cellular-level interactions associated with a hypertensive predisposition to AF, including systematic inflammation and the role of genetics, the impact of blood pressure variations on AF risk, and individualised treatment strategies specifically targeting the shared mechanisms, simultaneously propagating hypertension and AF.

Atrial fibrillation (AF) is the most common chronic arrhythmia and is a leading cause of stroke, with well-documented differences in pathophysiology, clinical manifestations, and prognosis according to the sex of the patient. This review provides an overview of known or hypothesized sex differences in physiology and stroke risk for patients with AF. Women are reported to have more extensive fibrosis of the left atrium, different functional properties of the atria, and higher sensitivity to prothrombotic stimuli, especially after menopause. Variations in stroke risk with AF are linked to age, hypertension, diabetes, and chronic kidney disease; overall, women have worse outcomes. The widely clinically implemented CHA₂DS₂-VASc score no longer considers sex as a variable, and its propriety for women is still debated. However, women are usually under prescribed anticoagulation despite having a higher long-term risk of stroke compared to men, suggesting a lack of equity of treatment for certain patient groups. New AI-based risk stratification models and precision medicine approaches are potentially useful in reducing these gaps. Future work should also aim to improve sex-based predictive models, considering different gender categories, and understanding the part played by hormonal alterations, atrial structural alterations, and thromboembolic risk in the treatment of AF.

Cardiac implantable electronic devices (CIEDs) such as pacemakers, implantable cardioverter-defibrillators (ICDs), and cardiac resynchronisation therapy (CRT) devices are generating unprecedented volumes of data in both inpatient and remote settings. Artificial intelligence (AI) techniques are increasingly being applied to enhance the management of these devices and the patients who rely on them. Recent advances demonstrate that machine learning (ML) and deep learning (DL) can improve diagnostic capabilities (for example, by detecting arrhythmias and predicting clinical events), streamline remote monitoring workflows, and optimise device-based therapies. Key applications include AI-driven algorithms that accurately detect true arrhythmias while filtering out false alerts from pacemakers and implantable monitors, neural network models that predict ventricular arrhythmias weeks before ICD shocks, and personalised models that forecast which heart failure patients will respond to CRT. Moreover, novel approaches such as natural language processing (NLP) and reinforcement learning are being explored to integrate diverse data sources and to enable devices to self-adjust their programming. This narrative review summarises the major applications of AI in the CIED domain—diagnostics, remote monitoring, and therapy optimisation—with an emphasis on the recent literature over the past five years. The review highlights important studies and randomised trials in each area, discusses the variety of AI techniques employed, and outlines future directions and challenges (including data standardisation, validation in clinical trials, and regulatory considerations) for translating these innovations into routine clinical care.

Sudden cardiac death (SCD) from ventricular arrhythmias (VAs) remains a leading cause of mortality worldwide. Traditional risk stratification, primarily based on left ventricular ejection fraction (LVEF) and other coarse metrics, often fails to identify a large subset of patients at risk and frequently leads to unnecessary device implantations. Advances in artificial intelligence (AI) offer new strategies to improve both long-term SCD risk prediction and near-term VAs forecasting. In this review, we discuss how AI algorithms applied to the 12-lead electrocardiogram (ECG) can identify subtle risk markers in conditions such as hypertrophic cardiomyopathy (HCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and coronary artery disease (CAD), often outperforming conventional risk models. We also explore the integration of AI with cardiac imaging, such as scar quantification on cardiac magnetic resonance (CMR) and fibrosis mapping, to enhance the identification of the arrhythmogenic substrate. Furthermore, we investigate the application of data from implantable cardioverter-defibrillators (ICDs) and wearable devices to predict ventricular tachycardia (VT) or ventricular fibrillation (VF) events before they occur, thereby advancing care toward real-time prevention. Amid these innovations, we address the medicolegal and ethical implications of AI-driven automated alerts in arrhythmia care, highlighting when clinicians can trust AI predictions. Future directions include multimodal AI fusion to personalise SCD risk assessment, as well as AI-guided VT ablation planning through imaging-based digital heart models. This review provides a comprehensive overview for general medical readers, focusing on peer-reviewed advances globally in the emerging intersection of AI, VAs, and SCD prevention.

Atrial fibrillation (AF) is the most common sustained arrhythmia associated with significant morbidity and mortality. External direct current cardioversion (DCCV) is a cornerstone intervention for rhythm control in AF; however, its success is influenced by various patient-specific and procedural factors. This review examines the predictors of DCCV success and AF recurrence with specific focus upon demographics, biochemical, cardiovascular imaging, and P-wave parameters and their likely ability to predict procedural outcomes. Demographic factors such as age, sex, and comorbidities influence DCCV outcomes, with prolonged AF duration, obesity, and heart failure being associated with higher failure rates. Elevated biochemical markers of inflammation and fibrosis, including C-reactive protein, galectin-3, and Type III procollagen-N-peptide, were predictive of poor outcomes. Imaging parameters, particularly left atrial (LA) volume and strain, emerged as critical indicators of atrial remodelling and DCCV failure. Increased P-wave duration and dispersion on electrocardiography were associated with an increased risk of recurrence. Biphasic waveforms and antiarrhythmic drugs, such as amiodarone and flecainide, improved cardioversion success. The predictors of DCCV success and recurrence reflect the interplay of structural, biochemical, and electrical remodelling in AF. Integrating these parameters into clinical practice can guide individualised patient management and improve outcomes. Further research is needed to validate these predictors and enhance precision medicine approaches in DCCV.