Current State of Artificial Intelligence in Assessing Cardiac Function

Area of Science:

• Cardiovascular Medicine
• Artificial Intelligence
• Medical Imaging
• Digital Health

Background:

• Accurate cardiac function assessment is crucial for cardiovascular disease (CVD) management.
• Artificial intelligence (AI) is increasingly applied across various medical data modalities.
• Cardiovascular medicine utilizes electrocardiography (ECG), echocardiography, cardiac CT/MRI, and electronic health records (EHR).

Purpose of the Study:

• To review recent advancements in AI applications within cardiovascular medicine.
• To synthesize AI's impact on analyzing ECG, echocardiography, cardiac CT/MRI, and EHR data.
• To highlight AI's role in diagnosing, managing, and monitoring cardiovascular conditions.

Main Methods:

• Review of current literature on AI in cardiovascular medicine.
• Synthesis of AI applications across major cardiovascular data modalities.
• Analysis of deep learning algorithms and foundation models in cardiology.

Main Results:

• Deep learning models achieve high accuracy in cardiac disease assessment across modalities.
• AI excels at detecting subclinical disease, occult etiologies, and ventricular dysfunction.
• AI models demonstrate comparable or superior performance to clinicians in specific tasks (e.g., MI detection, LVEF quantification).
• Foundation models and multimodal architectures accelerate learning, enable automated reporting, and support population screening.
• AI is transitioning from research to clinical practice, with potential for equitable care delivery.

Conclusions:

• AI is becoming an indispensable clinical tool in cardiology.
• Further validation through multicenter trials and open-source transparency is necessary.
• AI promises scalable screening, precision phenotyping, and improved cardiovascular care, particularly in resource-limited settings.