Artificial intelligence (AI) is transforming many fields, including healthcare, by enabling sophisticated analysis of vast amounts of information. In cardiology, AI uses computer algorithms to interpret and learn from heart-related data. This application, often called “Heart AI,” processes complex patterns within patient information to assist medical professionals. Its goal is to enhance the understanding of heart conditions and refine approaches to patient well-being.
AI in Cardiac Diagnostics
Artificial intelligence significantly aids in identifying existing heart conditions by analyzing various types of medical data. AI algorithms can interpret electrocardiograms (ECGs), which record the heart’s electrical activity, to detect irregularities such as atrial fibrillation or other arrhythmias. These systems process ECG readings rapidly, often identifying subtle patterns difficult for human interpretation alone, assisting cardiologists in making accurate diagnoses. For instance, AI algorithms have demonstrated high accuracy in detecting paroxysmal atrial fibrillation, achieving approximately 79% accuracy in identifying subclinical changes not visible during active episodes.
AI also processes medical images like echocardiograms and computed tomography (CT) scans to assess the heart’s structure and function. AI algorithms analyze these images to measure the heart’s pumping efficiency, detect structural abnormalities, or identify plaque buildup in coronary arteries. For example, AI-powered systems can quantify coronary artery calcium scores and analyze plaque composition from CT angiography (CTA) images, providing detailed insights into atherosclerosis. This advanced analysis helps physicians visualize the heart’s condition with greater precision.
AI can also extract features from ECG signals not visible to the human eye, such as heart rate variability, or detect cardiac structural damage like myocardial hypertrophy. AI-enhanced ECGs have shown the ability to spot early changes in heart structure related to valve defects, predicting conditions like regurgitant valvular heart diseases with 69-79% accuracy years before symptoms appear. This capability allows for earlier detection of issues that might otherwise go unnoticed, providing valuable information for a cardiologist’s assessment.
Predictive Analytics for Heart Disease
Artificial intelligence excels at forecasting future cardiac events by analyzing extensive patient datasets. AI systems integrate diverse information, including electronic health records (EHRs), genetic profiles, and lifestyle factors, to assess an individual’s propensity for developing heart disease. This comprehensive analysis allows for the identification of patients at elevated risk for events such as heart attacks or heart failure years in advance. By identifying these patterns, AI enables a proactive approach to care, focusing on prevention rather than solely on treatment after an event occurs.
AI models can process thousands of data points per person from EHRs, including lab test results, medications, previous diagnoses, and daily habits, to create personalized risk scores. For example, a “meta-prediction” model combining 22 genetic risk scores, 13 clinical values, and 15 “meta-features” demonstrated improved accuracy in predicting the 10-year risk of coronary artery disease, outperforming traditional risk calculators. This capability allows AI to uncover intricate risk patterns often imperceptible through conventional methods.
AI can also analyze data from routine chest CT scans to predict cardiac-related mortality and identify patients at high risk for heart disease. These systems can quantify coronary artery calcium, assess heart shape, and integrate body composition data to refine risk estimations. Such predictive insights allow healthcare providers to intervene earlier, potentially leading to lifestyle modifications or preventative treatments that could avert future cardiovascular complications.
AI-Guided Treatment and Management
Artificial intelligence extends its support beyond diagnosis and risk assessment, playing a role in personalizing and optimizing patient care. Once a heart condition is identified or a risk assessed, AI can help physicians select suitable medications and determine precise dosages. By analyzing a patient’s unique genetic profile and how they metabolize certain drugs, AI-driven pharmacogenetics aims to enhance medication safety and effectiveness, reducing reliance on trial-and-error prescribing. For instance, systems like CURATE.AI use patient-specific data, such as drug doses and corresponding physiological responses, to dynamically recommend personalized anti-hypertensive doses.
AI also assists in planning complex cardiac procedures, providing surgeons with detailed insights and tools for greater precision. It can generate highly detailed, interactive 3D digital models of a patient’s heart from CT or MRI images, allowing clinicians to plan and visualize surgeries before they occur. This technology has been shown to reduce procedure time significantly, such as by 60% in ventricular tachycardia ablations. Additionally, AI can guide robotic-assisted surgery by processing real-time images from endoscopes or other devices, offering visual assistance and enabling surgical robots to operate with precision that surpasses human dexterity.
These AI applications contribute to more informed decision-making during the treatment phase. AI-enabled coronary plaque analysis from CT angiography can influence management strategies for symptomatic patients, often leading to adjustments in preventative therapies, such as the addition or intensification of lipid-lowering agents. This personalized approach helps tailor interventions to a patient’s specific disease characteristics, aiming to improve outcomes and potentially reduce long-term healthcare costs.
The Role of Wearable Technology
Consumer-level wearable devices, such as smartwatches, have become an accessible tool for early heart health screening. These devices typically use photoplethysmography (PPG) technology, which measures changes in blood volume through the skin, to detect heartbeat signals and identify irregularities. Many smartwatches, including models from Apple and Samsung, offer features like on-demand electrocardiograms (ECGs) and irregular heart rhythm notifications.
The AI embedded in these wearables continuously monitors heart rhythms in the background, often prompting users with alerts if an irregular pattern suggestive of atrial fibrillation (AFib) is detected. When an alert occurs, the device may encourage the user to take an on-demand ECG recording, which can then be shared with a healthcare professional for further evaluation. This two-stage detection system, combining continuous PPG monitoring with on-demand ECG, leverages the convenience of wearables for early detection.
While these devices are effective screening tools, they do not provide a definitive diagnosis. For example, in studies, irregular heart rhythm notifications from wearables led to AFib detection on a subsequent ECG patch monitor in about 32% of individuals. The primary purpose of a notification from a smartwatch is to prompt the user to seek medical attention from a doctor for a proper clinical assessment and diagnosis, rather than serving as a standalone diagnostic tool.