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Agents Under the Curve (AUC)

AI Summary: The article addresses the disconnect between agentic AI systems in healthcare, which typically produce binary outputs, and the standard evaluation metric of Area Under the Curve (AUC), which requires continuous scores for effective risk assessment. It highlights that while agentic models are gaining traction, they often do not provide the calibrated probabilities necessary for AUC calculations, thereby complicating comparisons with traditional medical models. The author proposes practical strategies for converting binary outputs from these systems into continuous scores, aiming to facilitate valid AUC comparisons and enhance the understanding of the performance of agentic solutions in clinical settings. This work seeks to bridge the gap between emerging AI methodologies and established evaluation standards in medical prediction.