The Role of Artificial Intelligence in Improving Diagnosis, Management, and Outcomes of Acute Myocardial Ischemia: A Systematic Review

Abstract

Artificial intelligence (AI) has emerged as a transformative force in cardiovascular medicine, particularly in the diagnosis, management, and prognostication of acute myocardial ischemia (AMI). This systematic review synthesizes current evidence on AI applications across diagnostic modalities, risk stratification, therapeutic decision-making, and outcome prediction in AMI. A total of 30 peer-reviewed studies were included, encompassing machine learning (ML), deep learning (DL), and hybrid models applied to electrocardiography (ECG), imaging, and electronic health records (EHRs). AI demonstrated superior diagnostic accuracy, enhanced triage efficiency, and improved prognostic modeling compared to conventional methods. Notably, AI-enabled ECG interpretation and coronary imaging have shown cardiologist-level performance in detecting ischemia. Risk prediction models using ML have outperformed traditional scoring systems, while AI-driven decision support tools have optimized therapeutic pathways. Despite promising results, challenges remain in clinical integration, interpretability, and generalizability. This review underscores the potential of AI to revolutionize AMI care and highlights future directions for research, validation, and ethical implementation.

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