Key result
AI models achieve an AUC of ~0.82 for diagnosing major cardiovascular conditions.
Why the study?
Cardiovascular disease poses a substantial global health burden with diagnostic delays compromising outcomes, prompting evaluation of the diagnostic effectiveness of artificial intelligence models across major cardiovascular conditions.
Do artificial intelligence models provide effective diagnostic performance for major cardiovascular conditions?
Meta-Analysis (n=35)
Do artificial intelligence models provide effective diagnostic performance for major cardiovascular conditions?
AI models show promising diagnostic discrimination for cardiovascular diseases, but high heterogeneity and lack of external validation highlight the need for local calibration before clinical implementation.
Answers are grounded in this paper and cite their sources.
AI models show promising discrimination for CAD, ACS/MI, and HF; leaves open routine adoption pending external validation and local calibration.
Introduction. Cardiovascular disease represents a substantial global health burden, and delays in diagnosis can further compromise clinical outcomes. Objective. This review aimed to evaluate the diagnostic effectiveness of artificial intelligence models across major cardiovascular conditions, with a specific focus on coronary artery disease, acute coronary syndromes and myocardial infarction, and heart failure. Method. Following PRISMA guidelines, we conducted a systematic review and applied meta-analytical techniques to pool measures of diagnostic performance. Random effects models with restricted maximum likelihood and Hartung–Knapp adjustments were used to calculate summary estimates, and analyses were stratified by cardiovascular condition. Only effects with variance (reported 95% CI or derivable denominators) were pooled. Areas under the receiver operating characteristic curve without variance were synthesized narratively. Results. Out of 39,383 records, thirty-five studies were eligible. The pooled area under the receiver operating characteristic curve was 0.823 (95% confidence interval 0.754–0.892). Heterogeneity was considerable (I² = 98.4%), and the 95% prediction interval was 0.59 to 1. However, 9 of 35 included studies reported external/independent validation. The pooled estimate therefore represents an overall descriptive average, reflecting the expected range of diagnostic performance rather than a single universal value. Stratified results showed pooled values of 0.88 for acute coronary syndromes and myocardial infarction, 0.87 for heart failure, and 0.86 for coronary artery disease. Sensitivity often reached 0.90 or higher in electrocardiography deep learning models, while specificity remained variable. Conclusion. Artificial intelligence models demonstrated promising discrimination across cardiovascular conditions, with variable specificity and predictive value, indicating that clinical utility requires external validation and local calibration.
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Zepeda-Lugo et al. (2025) conducted a meta-analysis in Cardiovascular disease (coronary artery disease, acute coronary syndromes, myocardial infarction, and heart failure) (n=35). Artificial intelligence models was evaluated on Diagnostic performance (pooled area under the receiver operating characteristic curve) (Pooled AUC 0.823, 95% CI 0.754-0.892). Artificial intelligence models demonstrated a pooled area under the receiver operating characteristic curve of 0.823 for diagnosing major cardiovascular conditions.
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