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相关概念视频

Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...

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相关实验视频

Updated: Jun 23, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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大型语言模型,人类专家和专家编辑的大型语言模型对神经眼科问题的比较研究

Prashant D Tailor1, Lauren A Dalvin, Matthew R Starr

  • 1Department of Ophthalmology (PDT, LAD, MRS, DAT, KDC, MCB, SAM, JJC), Mayo Clinic, Rochester, Minnesota; Departments of Ophthalmology (HEM) and Neurology & Neurological Sciences (HEM), Stanford University, Palo Alto, California; Department of Ophthalmology (KEL, MWK, DDM), Glick Eye Institute, Indiana University School of Medicine, Indianapolis, Indiana; Ophthalmology Service (KEL), Richard L. Roudebush Veterans' Administration Medical Center, Indianapolis, Indiana; Department of Ophthalmology and Visual Sciences (KEL), University of Louisville, Louisville, Kentucky; Midwest Eye Institute (KEL), Carmel, Indiana; Circle City Neuro-Ophthalmology (KEL), Carmel, Indiana; Department of Neurology (MWK, DDM), Indiana University, Indianapolis, Indiana; Department of Ophthalmology (MADN, OMD), Mayo Clinic, Scottsdale, Arizona; and Department of Ophthalmology (MLP, ERE), Mayo Clinic, Jacksonville, Florida.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
|April 2, 2024
PubMed
概括
此摘要是机器生成的。

在神经眼科中,专家编辑的大型语言模型 (LLM) 响应获得了比人类专家单独获得的更高质量和同理心分数. 这表明,当专家对LLM进行改进时,它具有临床应用的巨大潜力.

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科学领域:

  • 医疗人工智能 医疗人工智能
  • 临床信息学 临床信息学
  • 神经眼科神经眼科

背景情况:

  • 与人类专家相比,大型语言模型 (LLM) 在医学中的有效性尚未得到充分证实.
  • 这项研究调查了由人类专家,LLM和专家增强的LLM在神经眼科中产生的反应的质量和同理心.

研究的目的:

  • 为了比较人类专家,各种大型语言模型 (LLM) 和专家编辑的LLM输出响应的质量和同理心.
  • 评估不同LLM (ChatGPT-3.5,ChatGPT-4,Claude 2,Bing,Bard) 在专业医疗领域的表现.

主要方法:

  • 一项随机化,面具化,多中心的横截面研究,涉及13名神经眼科专家.
  • 专家们回答了21个神经眼科问题,并编辑了ChatGPT-4的响应,记录了响应时间.
  • 来自Expert + AI,人类专家和5名LLM的匿名响应被12名盲人质量和同理心 (1-5级) 专家评估.

主要成果:

  • 专家+人工智能响应在质量 (4.16 ± 0.81) 和同理 (3.63 ± 0.87) 两方面都获得了最高分.
  • 专家编辑的LLM响应在质量 (P < 0.0001) 和同理心 (P = 0.002) 方面明显优于人类专家响应.
  • 聊天GPT-4和聊天GPT-3.5表现强,而Bard和Bing也表现出了竞争力的结果.

结论:

  • 与人类专家相比,专家编辑的大型语言模型 (LLM) 响应显示出更高的质量和同理心.
  • 这些发现支持进一步调查专家精炼的LLMs的整合,以加强神经眼科的临床实践.