Evolving Insights: The AUC/c-Index in Modern Prognostic Model Evaluation

In the AI era, prognostic and diagnostic models must be adaptable and capable of performing well in diverse and dynamic settings. Traditional metrics like the AUC (Area Under the Receiver Operating CharacteristicsCurve), c-index, and calibration tools remain the predominant methods for model validation. These metrics, while useful for assessing model discrimination and calibration, are not designed to capture the complexity required to ensure models remain effective across different populations and clinical scenarios. To ensure that AI-driven models offer meaningful improvements in patient care, a comprehensive and clinically relevant discussion of utility of AUC and other statistics for model evaluation is essential. This session aims to odiscuss important aspects of model performance that are important to understand to better address AI-era needs specific for healthcare settings.