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Updated: Jun 26, 2025

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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大型语言模型对神经病理护理质量和效率的影响:神经病学新兴问题

Lidia Moura1, David T Jones1, Irfan S Sheikh1

  • 1From the Center for Value-based Health Care and Sciences (L.M.), and Department of Neurology (L.M., S.M.), Massachusetts General Hospital, Boston; Harvard Medical School (L.M., S.M.), Boston, MA; Department of Neurology (D.T.J., L.K.J.), Mayo Clinic, Rochester, MN; Department of Neurology (I.S.S.), University of Texas Southwestern Medical Center, Dallas; Department of Neurology (M.K.), University of Pennsylvania Health System, Philadelphia; Department of Neurology (B.R.K.), Icahn School of Medicine at Mount Sinai, New York, NY; Information Technology Division (A.L.W.), Cleveland Clinic, OH; Department of Pediatrics (Z.M.G.), Weill Cornell Medicine, New York, NY; American Academy of Neurology (H.M.S.), Minneapolis, MN; and The Center for Clinical Excellence (A.D.P.), Nationwide Children's Hospital, Division of Neurology, The Ohio State University College of Medicine, Columbus.

Neurology
|May 17, 2024
PubMed
概括

大型语言模型 (LLM) 在神经学中提供了人工智能驱动的潜力,但面临临临床挑战. 解决推理,偏见和成本方面的局限性对于安全有效地整合到患者护理中至关重要.

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