Automated detection of atrial fibrillation based on vocal features analysis

State of the art of mobile health technologies use in clinical arrhythmia care

The rapid growth in consumer-facing mobile and sensor technologies has created tremendous opportunities for patient-driven personalized health management. The diagnosis and management of cardiac arrhythmias are particularly well suited to benefit from these easily accessible consumer health technologies. In particular, smartphone-based and wrist-worn wearable electrocardiogram (ECG) and photoplethysmography (PPG) technology can facilitate relatively inexpensive, long-term rhythm monitoring. Here we review the practical utility of the currently available and emerging mobile health technologies relevant to cardiac arrhythmia care. We discuss the applications of these tools, which vary with respect to diagnostic performance, target populations, and indications. We also highlight that requirements for successful integration into clinical practice require adaptations to regulatory approval, data management, electronic medical record integration, quality oversight, and efforts to minimize the additional burden to health care professionals.

Artificial intelligence in cardiovascular medicine: clinical applications

Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.

Promises and Perils of Consumer Mobile Technologies in Cardiovascular Care: JACC Scientific Statement

Direct-to-consumer (D2C) wearables are becoming increasingly popular in cardiovascular health management because of their affordability and capability to capture diverse health data. Wearables may enable continuous health care provider-patient partnerships and reduce the volume of episodic clinic-based care (thereby reducing health care costs). However, challenges arise from the unregulated use of these devices, including questionable data reliability, potential misinterpretation of information, unintended psychological impacts, and an influx of clinically nonactionable data that may overburden the health care system. Further, these technologies could exacerbate, rather than mitigate, health disparities. Experience with wearables in atrial fibrillation underscores these challenges. The prevalent use of D2C wearables necessitates a collaborative approach among stakeholders to ensure effective integration into cardiovascular care. Wearables are heralding innovative disease screening, diagnosis, and management paradigms, expanding therapeutic avenues, and anchoring personalized medicine.