Integration of Consumer Wearables and AI in Cardiovascular Risk Assessment: Opportunities & Challenges

Abstract

Background:Continuous monitoring of Physiological parameters can now be continuously monitored by the increasing number of
consumer styles of wearables, including smartwatches and moreover fitness trackers. These devices along with the emerging artificial
intelligence (AI) algorithms may provide new opportunities to improve cardiovascular risk assessment outside of the traditional clinic
interaction.
Objective:To describe and analyze the possible opportunities, methods, and limitations that can be involved in making consumer
wearable data and AI-based analytics part of the modern cardiovascular risk assessment models.
Methods:The synthesis is based on the latest changes in wearable sensing technology, risk prediction model built on artificial
intelligence, and strategies of digital health integration. The data acquisition quality, algorithmic processing of longitudinal physiological
signals, validation frameworks and interoperability with electronic health records are considered as the key aspects.
Results:Wearables were also found to be highly accurate in measuring heart rate, number of steps and detected atrial fibrillation with an
agreement-rates of 88 to 94 as known by Clinical standards. The high predictive performance achieved by AI models such as CNNs,
LSTMs, and hybrid clinical-wearable models proved to be strong in terms of arrhythmias, the threat of hypertension, and early heartfailure decompensation AUC of 0.83 and 0.93, respectively. Due to this, however, blood-pressure estimation, reliability of HRV,
variability in quality of data, and cross-device reproducibility were observed to be limited.
Conclusion:Wearables provided to consumers together with AI can be seen as a groundbreaking voice of providing real-time,
personalized cardiovascular risk monitoring. The potential to record real-time physiological functions can enhance early intervention of
arrhythmias, high blood pressure, and dysfunction of subclinical cardiovascular functions. Nonetheless, this should be overcome by issues
such as inconsistent data accuracy, algorithm bias, data privacy, and insufficient regulatory transparency in order to advance safe and fair
adoption. Sealing these loopholes by instituting tight validation, standard interventions, and open governance will be important to their
successful introduction into the mainstream cardiovascular care