Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM)

A rtificial intelligence (AI) tools for radiology are commonly unmonitored once deployed . Th e lack of real - time case - by - c ase assessments of AI prediction confidence require s users to independently distinguish between trustworthy and unreliable AI predictions, which increas es cognitive burden, r educ es productivity, and potentially lead s to misdiagnos e s. To address these challenges, we introduce Ensembled Monitoring Model (EMM), a framework inspired by clinical consensus practices using multiple expert reviews. Designed specifically for black - box commercial AI products, EMM operates independently without requiring access to interna l AI components or intermediate outputs, while still providing robust confidence measurements. Using intracranial hemorrhage detection as our test case on a large, diverse dataset of 2919 studies, we demonstrate that EMM successfully categorizes confidence in the AI - generated prediction, suggesting different actions and helping improve the overall performance of AI tools to ultimately reduc e cognitive burden . Importantly, we provide key technical considerations and best practices for successfully translating EMM into clinical settings .