The Effectiveness of Artificial Intelligence in the Early Detection of STEMI: A Systematic Review - Rizki Nur Azhadin; Sriyono Sriyono; Erna Dwi Wahyuni (Jurnal Ners, 2026)
Abstract Introduction: ST-Elevation Myocardial Infarction (STEMI) is one of the most critical manifestations of acute coronary syndrome and requires rapid diagnosis and timely intervention to reduce morbidity and mortality. Recent advances in Artificial Intelligence (AI), particularly in machine learning and deep learning, offer promising solutions for improving the accuracy and speed of early STEMI detection through real-time analysis of electrocardiogram (ECG) and clinical data. Methods: A comprehensive literature search was conducted using five major databases (Scopus, PubMed, ProQuest, ScienceDirect, and Web of Science) for peer-reviewed articles published between 2015 and 2025. Study selection followed the PRISMA guidelines, and methodological quality was evaluated using the Joanna Briggs Institute (JBI) critical appraisal checklist. Data synthesis was performed narratively, focusing on diagnostic accuracy, clinical outcomes, and AI application types. Results: Fifteen studies met the inclusion criteria. The majority reported that AI-based models, particularly those using ECG input, achieved diagnostic accuracies exceeding 90%. Several studies demonstrated that AI outperformed traditional risk scoring systems like the TIMI score. Additionally, AI applications were shown to support remote monitoring and facilitate timely interventions in emergency cardiac care settings. Discussion: The findings of this review indicate that artificial intelligence has substantial potential to enhance early STEMI detection through improved diagnostic accuracy and faster clinical decision-making. AI-based ECG interpretation demonstrated superior performance compared to traditional diagnostic methods, which may reduce delays in reperfusion therapy and improve patient outcomes. Furthermore, AI integration in prehospital settings and remote monitoring systems supports early triage and timely intervention, particularly in resource-limited environments. However, challenges such as data heterogeneity, limited external validation, and ethical considerations remain important barriers to widespread implementation. These findings highlight the need for further large-scale multicenter studies to confirm the clinical effectiveness of AI-based diagnostic tools in real-world emergency cardiac care. Conclusion: AI has demonstrated high potential in improving early STEMI diagnosis, accelerating clinical decision-making, and enhancing patient outcomes in acute cardiac emergencies. However, successful implementation requires addressing external validation, data quality, clinician training, and ethical concerns. Integrating AI into emergency cardiac nursing practice may transform the delivery of care in resource-limited and time-sensitive environments.