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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

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Current State of Artificial Intelligence in Assessing Cardiac Function.

Victoria Yuan^1,2, Keane Lee³, Andrew P Ambrosy³

¹David Geffen School of Medicine, University of California, Los Angeles, USA.

Current Cardiology Reports

|November 27, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) enhances cardiovascular disease diagnosis by accurately analyzing diverse data like ECGs and echocardiograms. AI shows promise in early detection and improving patient care, especially in underserved areas.

Keywords:

Artificial intelligence Automated diagnosis Deep learning Foundation model Machine learning Transthoracic echocardiography

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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.