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

## TL;DR

This paper reviews how artificial intelligence improves diagnosis and treatment of heart attacks, showing AI can perform as well as experts and enhance patient outcomes.

## Contribution

The paper systematically reviews AI applications in acute myocardial ischemia, highlighting novel AI models that match or exceed traditional diagnostic and prognostic methods.

## Key findings

- AI models achieved cardiologist-level performance in interpreting ECGs and coronary imaging for detecting ischemia.
- Machine learning risk prediction models outperformed traditional scoring systems in AMI prognosis.
- AI-driven decision support tools improved therapeutic pathways and triage efficiency in AMI management.

## 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.