提示专业技巧! 提示专业技巧! 创建临床叙述总结的最佳实践
Darshan Thota1, David Alt2, Jacob Cole3
1Joint Trauma System, San Antonio, TX 78234, United States.
Military medicine
|May 31, 2025
概括
结构化提示通过大型语言模型 (LLM) 改善了临床摘要生成,但需要仔细调整以减轻幻觉和偏差. 为了可靠的临床可用性,需要进一步开发.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
背景情况:
- 临床叙事总结对于军事卫生系统至关重要.
- 评估大型语言模型 (LLM) 的快速工程策略对于准确和公正的临床总结至关重要.
研究的目的:
- 为了比较详细与模糊提示在生成临床叙事摘要的性能.
- 评估LLM生成的摘要的准确性,偏见和意义.
主要方法:
- 八个匿名的LLM在八个临床场景中进行了测试.
- 使用了结构化和模糊的提示变化.
- 志愿临床医生评估了符合性,充分性,幻觉和偏见的反应.
主要成果:
- 这两种提示符类型都显示了类似的指令符合性 (64%).
- 结构化提示产生了更具临床充分性的摘要 (39%对9%) 但增加了幻觉和偏见.
- 没有经过测试的LLM可靠地产生了临床上可用的摘要.
结论:
- 结构化的提示提高了LLM临床总结质量,但需要减轻幻觉的缓解策略.
- 快速工程最佳实践可以提高输出,但需要谨慎确保源文档的真实性.
- 建议进一步调整,以实现LLMs可靠的临床效用.
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