An Innovative Approach to Cardiac Care: Using Artificial Intelligence to Predict and Prevent Sudden Cardiac Arrest

Sudden cardiac arrest (SCA) is a leading cause of mortality in the world that requires quick detection and immediate intervention. Traditional diagnostic measures are in most cases destined to overlook insidious pre-cardiac symptoms. Machine learning (ML) and deep learning (DL) are other methods of Artificial Intelligence (AI) that provide solutions to early prediction when real-time multimodal inputs and outputs are present (ECG, PPG, HRV, and EHRs). The AI is able to identify non-linear trends, which are not visible to traditional methods, to provide early risk assessment. This paper introduces a hybrid framework of AI that combines deep signal processing and clinical data mining to create personalized alerts and risk profiles. It also raises such issues as the security of data, transparency of the model, and clinical flexibility. The reinforcement of interdisciplinary work will be beneficial in improving AI-based systems, facilitating proactive cardiovascular care and cutting down SCA-induced fatalities.