Abstract
Ischemic heart disease (IHD) remains a leading cause of global morbidity and mortality, underscoring the need for rapid and accurate diagnostic strategies. Conventional methods, including electrocardiography (ECG), imaging, and biomarkers, are effective but limited by factors such as delayed biomarker elevation, reliance on expert interpretation, and variability across settings. Artificial intelligence (AI) offers new opportunities to enhance early detection and risk prediction by applying machine learning and deep learning to large, complex datasets. In ECG analysis, AI models consistently identify subtle ischemic patterns, including occlusive myocardial infarction, with accuracy that often rivals or exceeds clinicians. In imaging, AI enhances echocardiography, CT, MRI, and nuclear modalities by automating segmentation, strain analysis, and plaque quantification while reducing interpretation time. In biomarkers, AI augments traditional tools like troponins and enables the discovery of novel predictors through multi-omics and wearable data integration, supporting dynamic and individualized risk assessment. Despite promising results, most studies remain retrospective or single-center, with limited validation across diverse populations and healthcare environments. Key barriers include algorithm bias, generalizability, regulatory uncertainty, and limited clinician familiarity. Future progress will depend on multicenter trials, federated learning, explainable AI, and integration into existing workflows. In conclusion, AI has the potential to transform cardiovascular care by enabling earlier and more precise diagnosis of IHD and more personalized risk prediction. However, realizing this potential will require careful validation, equitable implementation, and collaboration across disciplines to ensure safe and effective adoption in clinical practice.