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Updated: May 5, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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准确的医学图表抽象的大型语言模型:在中风中实现可扩展和安全的AI部署.

Zhusi Zhong1, Carl M Porto1, David Hong1

  • 1From the Warren Alpert Medical School of Brown University (Z.Z., C.M.P, M.G., R.K., G.K., L.B., J.R.F., E.S., S.C., L.S., G.B., M.J., S.Y., Z.J., D.N.W.), Department of Radiology (Z.Z., G.B., M.J., Z.J.), Brown University Health, Department of Interventional Radiology (D.N.W.), Brown University Health, Department of Neurosurgery (J.R.F.), Brown University Health, Providence 02903, USA.

AJNR. American journal of neuroradiology
|February 20, 2026
PubMed
概括

大型语言模型 (LLM) 可以准确地从神经干预性中风报告中提取临床数据. 这种人工智能方法通过高效地构建复杂的医疗信息来帮助研究.

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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