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Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges
0
Zitationen
6
Autoren
2026
Jahr
Abstract
AI improves predictive accuracy in cardiovascular risk assessment. Despite strong discrimination performance (AUC), methodological heterogeneity, insufficient calibration assessment, algorithmic bias, limited external validation, and regulatory uncertainty remain major barriers to implementation. Clinical translation requires multicenter RCTs, explainable AI frameworks, and standardized reporting guidelines such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Artificial Intelligence (TRIPOD-AI).
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