A multimodal foundation model for emergency head CT interpretation
Zheng, J.; Chen, Y.; Wu, B.; Wang, Y.; Liu, M.; Li, L.; Jiang, S.; Chen, W.; Xu, L.; Wu, Y.; Liu, C.; Guo, L.; Bai, X.; Li, Z.; Yang, H.; Qin, F.; Liu, J.; Qu, H.; Liao, Q.; Zhao, G.; Pan, K.; Guo, J.; Chen, L.; Zhou, Y.; Sun, H.; Tian, Q.
Show abstract
Non-contrast head CT is the first-line imaging modality for acute neurological emergencies, with demand rising worldwide. However, existing foundation models for head CT interpretation are ill-suited for emergency use because they target general or chronic-disease assessment and optimize reports for lexical overlap rather than the risk-relevant findings central to emergency triage. Here we present CHIEF, a Chinese-language Head CT Interpretation Emergency Foundation model, pretrained on emergency head CT volumes and paired reports with contrastive, generative, and geometry-regularization objectives. Trained and evaluated on 16,563 examinations from seven hospitals, CHIEF achieved an AUROC of 0.9646 for emergency triage and drafted triage-oriented radiology reports, while also supporting image-to-text retrieval for reference-case support and zero-shot abnormality recognition. CHIEF generated reports of substantially higher quality than those from commercial multimodal large language models, which could not be reliably distinguished from human-written ones by radiologists in a blinded Turing test. Overall, CHIEF provides a generalizable foundation for emergency head CT interpretation and radiologist-in-the-loop clinical decision support.
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