A retrospective study of a Chinese vision-language large model for emergency 3D brain CT interpretation

Emergency brain computed tomography (CT) is the first line imaging modality for patients with acute neurological symptoms and trauma, where delayed or incomplete recognition of critical findings can directly compromise clinical outcomes. However, emergency CT interpretation and Chinese reporting remain highly variable under severe time constraints and heterogeneous institutional settings. In this study, we develop ERBrain, a multimodal large model specifically tailored for emergency brain CT, which jointly performs three-dimensional image understanding, Chinese radiology report generation, and emergency severity triage within a unified framework. ERBrain integrates volumetric visual representations with a Chinese large language model and explicitly prioritizes emergency critical signs through risk focused training objectives and a lightweight knowledge-augmented prompting strategy. Using more than 10,000 multicentre emergency CT studies, ERBrain achieved an accuracy of 0.943 and a balanced accuracy of 0.940 for three-level emergency triage and achieved the highest FIES-Avg clinical semantic score among the evaluated report-generation models in the in-distribution cohort. Across external data, ERBrain maintained favourable triage performance in two cross-institutional validation cohorts, whereas performance was lower but remained clinically informative in a third cohort characterized by an extremely low prevalence of Positive cases. These findings support further prospective evaluation of ERBrain as a radiology worklist prioritization and report-drafting assistant in heterogeneous emergency imaging settings.