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Gastroesophageal reflux disease, or GERD, is a persistent medical condition that affects many individuals worldwide. Its clinical manifestations can vary greatly, making diagnosis and management challenging for healthcare professionals. The following is a comprehensive overview of the clinical manifestations, assessment, and management strategies for GERD.
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胃肠病学中的大语言模型:系统审查

Eun Jeong Gong1,2,3, Chang Seok Bang1,2,3, Jae Jun Lee3

  • 1Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.

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概括
此摘要是机器生成的。

大型语言模型 (LLM) 在胃肠病学中显示出改善诊断和自动化文档的潜力. 解决数据隐私和准确性方面的挑战是成功将其整合到内镜实践中的关键.

关键词:
在这里,我们可以看到AIAIAI.在法学士 (LLM) 课程中.准确度 准确度 准确度 准确度人工智能的人工智能是人工智能.临床实践中的临床实践临床推理 临床推理数据隐私 隐私数据 隐私数据深度学习是一种深度学习.诊断 诊断 诊断 诊断 诊断 诊断诊断 诊断 诊断 的 诊断 诊断 诊断 诊断 的 诊断情绪支持 情绪支持通过内镜检查 (endoscopy) 进行内镜检查胃肠病学 胃肠病学大型语言模型患者参与 患者参与系统性审查 系统性审查

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用
  • 胃肠病学 胃肠病学

背景情况:

  • 医疗保健正在推进人工智能集成,特别是大型语言模型 (LLM).
  • 法学士课程为临床应用提供了理解和生成人类语言的能力.
  • 这些模型提供了提高医药患者护理和运营效率的机会.

研究的目的:

  • 系统地审查LLMs在胃肠病学和胃肠内镜中的作用.
  • 评估LLM应用程序,以提高诊断准确性,自动化文档,并加强专业教育和患者参与.

主要方法:

  • 在胃肠病学和胃肠内镜中对LLM的研究进行系统审查.
  • 搜索了MEDLINE,Embase和Cochrane中央数据库 (开始至2024年4月).
  • 包括英语,调查LLM潜力的全文研究,不包括案例报告和基础研究;用于质量评估的偏差风险工具.

主要成果:

  • 包括21项研究,由于异质性而导致叙事综合.
  • 在提供医疗信息,咨询建议,报告生成和诊断支持方面,LLM表现出能力.
  • 确定的主要挑战包括数据隐私,准确性问题以及跨学科合作的需要.

结论:

  • 临床临床医学提供了改变胃肠内镜实践的巨大潜力.
  • 与数据隐私,准确性和协作相关的挑战的导航对于有效的LLM实施至关重要.