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将大型语言模型集成到放射学教育中:一个以解释为中心的框架,用于增强学习,同时支持工作流程.

Shawn K Lyo1, Tessa S Cook2

  • 1Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, Baptist Health, Miami, Florida; Member, Society of Imaging Informatics in Medicine Members in Training Committee; American College of Radiology Informatics Advisory Council.

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概括

大型语言模型 (LLM) 可以通过在病例解释和分析期间提供实时支持来改善放射学教育. 这一框架提高了学员的学习和临床效率.

关键词:
人工智能的人工智能是人工智能.大型语言模型.精准教育是指精准教育.放射学教育 放射学教育

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科学领域:

  • 医学教育 医学教育
  • 人工智能在医学中的应用
  • 放射学 放射学是一门学科.

背景情况:

  • 放射学临床工作量增加限制了实习生监督和实时反.
  • 当前的教育方法很难跟上现代放射学实践的要求.

研究的目的:

  • 提出一个以解释为中心的框架,用于将大型语言模型 (LLM) 整合到放射学教育中.
  • 概述LLM如何在临床工作流程的不同阶段为放射学学员提供支持.

主要方法:

  • 一个分阶段的框架 (预测准备,主动指导,后指导分析) 用于LLM集成.
  • 士学位的功能包括案例总结,差异诊断支持和性能分析.

主要成果:

  • 法律学士可以根据教育价值提供背景意识的案例摘要和分类案例.
  • 实时指令支持包括差异诊断,完整性检查和结构化后续.
  • 指示后分析识别了差异,提出了改进建议,并跟踪了学员的进步.

结论:

  • 该框架提供了一个全面的方法,以利用LLMs在放射学教育.
  • LLM整合有潜力显著提高实习生学习,同时保持临床效率.