Artificial Intelligence in Echocardiography for Acute Coronary Syndromes: Current Evidence and Integration With Electrocardiography and Biomarkers

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ABSTRACT Background Early diagnosis of acute coronary syndromes (ACS) remains challenging because electrical, mechanical, and biochemical manifestations of myocardial ischemia evolve at different stages of the ischemic cascade. Although electrocardiography (ECG), cardiac biomarkers, and echocardiography are routinely used in clinical practice, these diagnostic modalities are commonly interpreted independently, potentially delaying recognition of myocardial ischemia. Recent advances in artificial intelligence (AI) offer new opportunities to improve diagnostic accuracy through multimodal integration, with echocardiography serving as the central imaging modality. Hypothesis AI‐enhanced echocardiography, integrated with electrocardiographic findings, cardiac biomarkers, and clinical information, may improve the early diagnosis, risk stratification, and clinical management of patients with suspected ACS compared with conventional sequential diagnostic approaches. Methods A comprehensive narrative review of contemporary literature was performed to evaluate current evidence regarding AI applications in echocardiography and their integration with ECG and cardiac biomarkers for the diagnosis of ACS. Published studies addressing automated image acquisition, chamber quantification, myocardial deformation analysis, regional wall‐motion assessment, multimodal machine‐learning models, explainable AI, and clinical implementation were critically reviewed and synthesized. Results AI has substantially expanded the capabilities of echocardiography by enabling automated image interpretation, quantitative assessment of ventricular function, myocardial strain analysis, and detection of regional wall‐motion abnormalities with high reproducibility. When integrated with ECG, serial high‐sensitivity cardiac troponin measurements, and clinical variables, multimodal AI systems demonstrate the potential to improve diagnostic accuracy, facilitate earlier identification of myocardial ischemia, reduce diagnostic uncertainty, and support individualized clinical decision‐making. Nevertheless, widespread implementation requires prospective multicenter validation, standardized imaging protocols, explainable algorithms, regulatory oversight, and seamless integration into clinical workflows. Conclusions AI‐enhanced echocardiography represents a promising step toward precision cardiovascular imaging and may become the central imaging component of future multimodal diagnostic pathways for patients with suspected ACS. Integration of structural, functional, electrical, biochemical, and clinical information through AI‐supported decision systems has the potential to improve diagnostic performance and optimize emergency cardiovascular care.