Artificial Intelligence Applications Across Cardiovascular Imaging Modalities

These modality-specific differences highlight that AI in cardiovascular care is far from uniform, with each imaging format shaping both the reliability and clinical impact of AI interventions.

The review analyzed randomized controlled trials published between 2021 and 2024 and found that ECG-based AI accounted for more than half of the trials and produced some of the most compelling clinical outcomes. Across six studies, ECG-driven tools reduced in-hospital mortality, improved detection of reduced ejection fraction, enhanced arrhythmia diagnosis, and increased recognition of left ventricular dysfunction in postpartum patients. ECG-based AI also shortened door-to-balloon times for patients with ST-segment elevation myocardial infarction (STEMI), although findings for long-term population monitoring were more variable. Despite these mixed results in broad screening settings, the consistency of benefits across acute and inpatient scenarios indicates that ECG data—being structured, high-frequency, and relatively standardized—provides a reliable foundation for AI-enabled risk detection and diagnostic support.

CT-based applications demonstrated a different form of effectiveness, one that aligned more with procedural decision-making and resource stewardship. These tools showed a notable ability to significantly reduce unnecessary invasive coronary angiography. In addition, CT-guided AI meaningfully increased the use of preventive therapies such as statins, reflecting greater clinician confidence in imaging-based risk characterization when supported by automated analysis. Device implantation outcomes also improved under CT-integrated AI guidance while reduced device use and manipulation. These findings suggest that CT-focused AI excels when integrated into structured procedural pathways, where detailed anatomical information allows AI algorithms to support technical decisions and reduce the need for invasive follow-up testing.

Echocardiography-based AI tools, while promising, demonstrated more heterogeneous results. Two studies evaluated AI-assisted echocardiographic acquisition and interpretation, revealing important workflow efficiencies but less uniform clinical impact. AI support reduced acquisition times and improved the reproducibility of measurements, especially in less experienced operators. However, substantial discrepancies persisted between initial assessments and final clinical evaluations in some trials. These gaps underscore the fundamental challenge of automating echocardiography—a modality that remains highly operator-dependent and prone to variability in image quality. While echocardiography benefits from real-time guidance and standardization through AI, large-scale trials suggest that the technology may require further refinement before clinical outcomes match the consistency seen with ECG-based interventions.

Cardiovascular Imaging Modalities – Evidence Gist

Taken together, the findings paint a nuanced picture of how AI interacts with different forms of cardiovascular imaging. ECG-based tools appear to be the most mature, driven by structured inputs and large training datasets, which translate into measurable improvements in mortality, arrhythmia detection, left ventricular dysfunction recognition, and rapid triage of myocardial infarction. CT-based applications deliver strong operational and procedural benefits, guiding decisions around coronary angiography and device implantation while elevating preventive care adoption. Echocardiography-based systems, although helpful in standardizing acquisition and reducing variability, exhibit wider performance swings and require more development before demonstrating similar reliability seen in ECG and CT tools.

For clinicians, the implications are clear: AI in cardiovascular imaging should not be viewed as a uniform innovation. Its value is heavily shaped by the modality in which it is deployed. As hospitals expand AI integration, decision-makers may benefit from prioritizing modalities where evidence is strongest and the risk of variability is lowest. ECG and CT currently provide the firm foundation for clinical and operational enhancement, while echocardiography represents a promising but evolving frontier. This modality-specific understanding will be essential as cardiology continues moving toward AI-supported diagnostic and procedural ecosystems.