Abstract
Clinical prediction models have progressed from traditional risk scores derived from epidemiological cohorts into sophisticated systems capable of integrating multimodal data, electronic health records, and machine learning approaches. This review provides a clinically focused synthesis of the clinical prediction model lifecycle, outlining their conceptual foundations and methodological evolution across health care settings, with particular emphasis on cardiovascular risk prediction. These predictive frameworks inform both population‐level public health strategies and individual clinical decision‐making. However, external validation studies frequently reveal a stark discrepancy between statistical performance and real‐world implementation, driven by temporal calibration drift, geographic transportability failures, and systemic sociodemographic or sex‐based biases. Importantly, clinical prediction models should not be viewed as replacements for randomized trial evidence; instead, they function as vital complementary mechanisms to dissect the heterogeneity of treatment effects and individualize treatment decisions by contextualizing average treatment effects at the bedside. As health care shifts toward data‐driven infrastructures, this review aims to contextualize the bench‐to‐bedside translational gap, offering an evidence‐based roadmap for clinicians, statisticians, and data scientists to safely harness predictive modeling and ensure meaningful improvements in patient outcomes.