Vision-Language-Based Large-Scale Model for Emergency Screening

This is a single-center, prospective, stratified 1:1 parallel-group randomized superiority diagnostic trial at The First Hospital of Jilin University. Eligible emergency patients aged ≥18 who received routine thoraco-abdominal non-contrast CT were randomly assigned to two groups. The experimental group adopted a self-developed vision-language large model (powER) for auxiliary CT interpretation, while the control group received standard radiological reading without AI assistance. All participants completed standardized 60-day clinical follow-up to obtain clinical gold-standard outcomes. The primary objective is to compare the diagnostic performance of the AI model versus conventional radiology interpretation for multiple acute thoraco-abdominal emergency lesions.