Artificial intelligence applications in emergency triage for suspected acute coronary syndrome: a systematic review

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: AUROC range 0.82-0.99 for AI models

• Primary Outcome: Diagnostic accuracy measured by AUROC

• Nnt Or Sensitivity: Sensitivity and specificity values not provided in abstract; treatment time reductions noted but not quantified

• Confidence Interval: Not reported in abstract

Clinical Application

Implementation challenges likely given need for integration with existing ED workflows and validation in local populations Relevant to Australian EDs but would require TGA approval for clinical decision support tools and integration with existing triage systems. RACGP guidelines for ACS management would need updating to incorporate AI-assisted triage recommendations. Emergency department patients with suspected ACS, though generalizability uncertain due to study heterogeneity

Abstract

BACKGROUND: Acute coronary syndromes (ACS) are time-critical conditions requiring rapid and accurate triage in the emergency department. Traditional triage may lead to delays, whereas artificial intelligence (AI) has the potential to enhance triage accuracy and efficiency. OBJECTIVE: This study aimed to synthesize and evaluate the effectiveness of AI applications in emergency triage or early clinical decision-making for suspected ACS, focusing on their impact on diagnostic accuracy, time to treatment, and operational outcomes. METHODS: This systematic review followed PRISMA guidelines. PubMed, Scopus, CINAHL, and IEEE Xplore were searched for studies published between 2020 and 2025. Eligible articles examined AI applications for triage of suspected ACS in the emergency department. Data on study design, AI models, comparators, inputs, and outcomes were extracted, and study quality was assessed using the Mixed Methods Appraisal Tool (MMAT). RESULTS: Fifteen studies from multiple countries and study designs were included. Convolutional neural networks and ensemble learning methods were the most used models. AI models generally demonstrated high diagnostic performance (AUROC 0.82-0.99), were associated with reductions in treatment times such as door-to-balloon and catheterization intervals, and showed potential to improve operational outcomes, including resource utilization and patient flow. However, findings varied across studies depending on model type, data inputs, and study design. CONCLUSIONS: AI-assisted triage for suspected ACS shows promise in supporting clinical decision-making and improving workflow efficiency. However, substantial heterogeneity and limited prospective validation suggest that findings should be interpreted cautiously. Further research is needed to confirm clinical effectiveness, generalizability, and safe implementation.

References

1. 1.Fatai, A., & Sattayarom, C. (2026). Artificial intelligence applications in emergency triage for suspected acute coronary syndrome: a systematic review. International Journal of Medical Informatics, 106368. https://doi.org/10.1016/j.ijmedinf.2026.106368