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?
Population
Thirty-five studies evaluating artificial intelligence models for major cardiovascular conditions
Design
Systematic review and meta-analysis
Key result
Artificial intelligence models demonstrated a pooled area under the receiver operating characteristic curve of 0.823 (95% CI 0.754-0.892) for diagnosing major cardiovascular conditions.
Authors
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AI models show promising discrimination for CAD, ACS/MI, and HF; leaves open routine adoption pending external validation and local calibration.
Meta-Analysis (n=35)
Do artificial intelligence models provide effective diagnostic performance for major cardiovascular conditions?
Effect estimate: Pooled AUC 0.823 (95% CI 0.754-0.892)
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.
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 (95% CI 0.754-0.892) for diagnosing major cardiovascular conditions.