Cardiovascular diagnosis and therapy · 2026
Artificial intelligence-enhanced electrocardiography for acute myocardial infarction detection: a systematic review.
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Lee Yangyoun, Ahn Jung Hwan, Yang Hyoung-Mo
Аннотация
BACKGROUND: Artificial intelligence (AI) shows promise for improving electrocardiogram (ECG)-based acute myocardial infarction (AMI) detection, but clinical readiness remains uncertain. We aimed to conduct a systematic synthesis informed by a structured search of AI-enhanced ECG systems to evaluate their performance, validation quality, and implementation readiness.
METHODS: We conducted a structured search of PubMed and Embase (publication date limits: January 1, 2017, to August 31, 2025; last searched: February 18, 2026) for English-language human studies that developed or validated AI models for ECG-based AMI diagnosis, extracting data on architectures, clinical applications, validation approaches, and performance metrics; we excluded non-original publications and studies that were non-English or non-human studies or did not use ECG input or did not address AMI diagnosis.
RESULTS: We included 88 studies; the total number of participants was not estimable because sample sizes were inconsistently reported across studies. Among 88 identified studies, convolutional neural networks predominated (51/88, 58%). Most studies were retrospective (80/88, 91%) and used 12-lead ECG (64/88, 73%). Reported performance varied widely [area under the receiver operating characteristic curve (AUROC), 0.700-0.999; sensitivity, 67.7-100.0%; specificity, 73.3-100.0%], with promising results for detecting subtle ischemic patterns in non-ST-elevation and occlusion myocardial infarction (OMI). However, only 33/88 (37.5%) performed external validation. Public datasets were used in 50/88 (57%) and institutional patient cohorts in 43/88 (49%); several studies used both sources. More complex architectures did not consistently demonstrate superior accuracy, though heterogeneity in study designs limits definitive conclusions.
CONCLUSIONS: AI demonstrates substantial technical potential for AMI detection, particularly for subtle ischemic patterns. However, critical gaps impede clinical deployment: insufficient external validation, reliance on curated datasets with limited generalizability, absence of standardized evaluation frameworks, and insufficient evidence on patient-centered outcomes. Future research must prioritize prospective multicenter validation, standardized benchmarks with rigorous reference standards, and real-world implementation studies examining clinical outcomes and workflow integration. Technical feasibility is established; clinical impact now depends on validation rigor and pragmatic deployment.
METHODS: We conducted a structured search of PubMed and Embase (publication date limits: January 1, 2017, to August 31, 2025; last searched: February 18, 2026) for English-language human studies that developed or validated AI models for ECG-based AMI diagnosis, extracting data on architectures, clinical applications, validation approaches, and performance metrics; we excluded non-original publications and studies that were non-English or non-human studies or did not use ECG input or did not address AMI diagnosis.
RESULTS: We included 88 studies; the total number of participants was not estimable because sample sizes were inconsistently reported across studies. Among 88 identified studies, convolutional neural networks predominated (51/88, 58%). Most studies were retrospective (80/88, 91%) and used 12-lead ECG (64/88, 73%). Reported performance varied widely [area under the receiver operating characteristic curve (AUROC), 0.700-0.999; sensitivity, 67.7-100.0%; specificity, 73.3-100.0%], with promising results for detecting subtle ischemic patterns in non-ST-elevation and occlusion myocardial infarction (OMI). However, only 33/88 (37.5%) performed external validation. Public datasets were used in 50/88 (57%) and institutional patient cohorts in 43/88 (49%); several studies used both sources. More complex architectures did not consistently demonstrate superior accuracy, though heterogeneity in study designs limits definitive conclusions.
CONCLUSIONS: AI demonstrates substantial technical potential for AMI detection, particularly for subtle ischemic patterns. However, critical gaps impede clinical deployment: insufficient external validation, reliance on curated datasets with limited generalizability, absence of standardized evaluation frameworks, and insufficient evidence on patient-centered outcomes. Future research must prioritize prospective multicenter validation, standardized benchmarks with rigorous reference standards, and real-world implementation studies examining clinical outcomes and workflow integration. Technical feasibility is established; clinical impact now depends on validation rigor and pragmatic deployment.