Prediction Is Not Detection: Evaluating Pre-Recognition Claims in Longitudinal Clinical AI
Jing Yang, Long R. Jiao, Xiujun Cai, Zongjiu Zhang
Abstract
Clinically useful early detection requires validated pre-recognition lead time. Yet event-based evaluations of longitudinal clinical AI can treat recognition-mediated care-process signals as shortcuts and recognition-dependent endpoints as reference standards, inflating apparent performance and lead time while undermining cross-center transport. Such results may serve prognosis without establishing detection before recognition. We define an interval-censored pre-recognition transition, an independent as-of reference standard, and a prespecified recognition proxy to make the claim testable.