Collaborative generalist-specialist AI for safe worklist deprioritization of non-contrast brain computed tomography

Safe deprioritization of non-contrast brain computed tomography (NCCT) demands high sensitivity and negative predictive value (NPV) across common and uncommon findings requiring clinical attention. Here, we present a collaborative safety net integrating BrainCT-CLIP, a generalist foundation model via contrastive language-image pre-training (CLIP), with commercial detectors. Every examination remained in the reporting workflow, deprioritized only when no model returned a positive result. In a retrospective multicenter external validation of consecutive emergency and trauma cohorts ( n = 2001), standalone commercial detectors left 31 of 324 remarkable examinations deprioritized, including 16 target-disease false negatives and 15 outside their indications. In contrast, the collaborative system recovered every remarkable examination at a threshold prespecified in the development cohort, achieving 100% sensitivity and NPV while deprioritizing 18% of examinations (368/2001; 95% CI, 17–20). These findings support generalist-specialist AI for safe worklist deprioritization under radiologist oversight, with high sensitivity for intracranial findings requiring clinical attention.