Transforming Cardiac Imaging With Artificial Intelligence: Automation, Precision, and Clinical Integration in...

Artificial intelligence is reshaping how we image the heart. This narrative review synthesizes evidence from 22 peer reviewed studies published between 2020 and 2026, identified through PubMed, Scopus, and Web of Science, examining AI applications across echocardiography and cardiac magnetic resonance (CMR). In echocardiography, AI enables automated image acquisition, chamber and valve segmentation, and left ventricular ejection fraction measurement with accuracy matching experienced echocardiographers, while also reducing interobserver variability and analysis time. Automated global longitudinal strain analysis has further improved detection of subclinical myocardial dysfunction, abnormalities that visual assessment routinely misses. In CMR, deep learning algorithms have demonstrated strong performance in cardiac chamber segmentation, myocardial tissue characterization, and multi-class disease classification. Wang et al. reported screening and diagnostic AUCs of 0.990 and 0.991 across eleven cardiovascular disease categories, while Diao et al. achieved AUCs of 0.895-0.980 for left ventricular hypertrophy classification. Beyond single-modality gains, AI-driven risk stratification models integrating imaging with clinical data have outperformed conventional scoring tools. These advances collectively improve diagnostic accuracy, workflow efficiency, and the capacity for personalized patient management. A limitation remains real and worth acknowledging. Heterogeneity in imaging protocols, insufficient cross-population validation, and limited algorithm transparency continue to restrict widespread clinical adoption. Achieving the full potential of AI in cardiac imaging will take more than good algorithms. It will require prospective validation, equitable dataset development, clearer regulatory pathways, and genuine collaboration between clinicians, engineers, and policymakers.