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.
Journal of imaging informatics in medicine
|February 2, 2024
概括
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.
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.
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