From Promise to Practice: Real-World Readiness of Artificial Intelligence for Cardiac Magnetic Resonance Imaging

Cardiac magnetic resonance imaging (CMR) provides a comprehensive evaluation of the cardiac morphology, ventricular function, and myocardial tissue characteristics within a single examination.Cine imaging, parametric mapping, and late gadolinium enhancement (LGE) together enable detailed phenotyping of a wide range of cardiovascular diseases.Despite these advantages, routine implementation of CMR remains limited in many clinical settings.Long examination times and labor-intensive postprocessing, which often require expert manual segmentation and interpretation, represent major barriers to broader adoption, particularly in high-volume clinical settings.Artificial intelligence (AI) has been proposed as a key enabler for addressing these challenges by improving the efficiency, standardization, and reproducibility of CMR analysis [1].Over the past decade, numerous AI-based approaches have demonstrated promising performance in segmentation, quantification, and pattern recognition tasks.However, the transition from algorithm development to routine clinical application has been slower than anticipated.The gap reflects the reality that successful clinical