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用大型语言模型为PET报告生成个性化印象.

Xin Tie1,2, Muheon Shin1, Ali Pirasteh1,2

  • 1Department of Radiology, School of Medicine and Public Health, University of Wissconsin, Madison, WI, USA.

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概括
此摘要是机器生成的。

大型语言模型 (LLM) 可以为正电子发射断层扫描 (PET) 报告产生临床有用的印象. 在89%的案例中,个性化LLM产生的印象被发现是临床上可接受的,有助于加快报告.

关键词:
信息学是一种信息学.大型语言模型自然语言处理自然语言处理.核医学就是核医学.定子发射断层扫描 (PET) 是一种定子发射扫描.放射学报告摘要 放射学报告摘要

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科学领域:

  • 医疗成像中的人工智能
  • 放射学自然语言处理用于放射学.

背景情况:

  • 大型语言模型 (LLM) 显示了将放射学发现总结为印象的潜力.
  • 对于全身PET报告的自动印象生成具有挑战性,并且研究不足.

研究的目的:

  • 评估LLM在PET报告中产生印象的临床实用性.
  • 为此任务微调和比较多个开源的LLM.

主要方法:

  • 十二个开源LLM在37,370个回顾性PET报告上进行了微调.
  • 模型包含了医生身份,以学习特定的报告风格.
  • 根据自动指标和医生偏好,PEGASUS被选为表现最佳的LLM.
  • 核医学医生根据质量维度和整体效用评估了临床效用.

主要成果:

  • 佩加索表现出强的表现,89%的个性化印象被认为是临床上可接受的.
  • 医生评价了个性化的LLM产生的印象,平均效用分数为4.08/5.5.
  • 这些个性化印象的实用性与医生指定的印象 (4.03/5).

结论:

  • 由PEGASUS LLM产生的个性化印象在临床上对PET报告有用.
  • LLM具有极大的潜力,可以大大加快PET印记的起草.
  • 这项技术可以通过为印记生成提供基础来帮助放射科医生.