AI-powered software scrutinises MRIs, CT scans, and echocardiograms, accurately spotting heart structure changes and arterial plaque. Predictive models leverage patient data to foresee CVD risks, aiding in early intervention planning. 

AI-powered diagnostics involve the use of machine learning algorithms to analyse large amounts of data from various sources such as medical records, genetic information, wearable devices and imaging scans. This enables the early detection of diseases by identifying patterns and anomalies that may go unnoticed by human physicians.

The integration of AI extends to risk stratification for CVDs as well. By considering factors like age, gender, and medical history, AI-powered diagnostic tools can predict the likelihood of an individual developing a specific type of CVD. This personalised approach enables physicians to tailor treatment plans based on individual risk factors, paving the way for more effective and personalised patient care. 

AI also aids in post-treatment or post-surgery care of cardiovascular patients by analysing vast amounts of patient data, including vital signs, lab results, and imaging scans. It can identify patterns and trends that might not be immediately obvious to clinicians, helping in the early detection of potential risks such as irregularities in heart rhythms, clot formation, or other complications.

AI also streamlines the workflow for healthcare professionals by automating tasks such as image analysis and data interpretation. 

Photos Courtesy: iStock