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将生成语言模型应用于整形外科的实践

Jessica Caterson¹, Olivia Ambler², Nicholas Cereceda-Monteoliva³

¹London School of Hygiene & Tropical Medicine, London, UK.

|March 14, 2024

概括

像ChatGPT和GPT-3这样的大型语言模型 (LLM) 可以为骨科场景生成可读且大多准确的临床字母. 虽然有效,但LLM有时会省略或添加不准确的信息,需要临床医生的监督.

科学领域:

人工智能在医学中的应用
在医疗保健中的自然语言处理.

背景情况:

大型语言模型 (LLM) 在各种文本生成任务中展示了潜力.
在临床文档和管理规划中应用LLMs需要进行彻底的评估.

研究的目的:

评估LLM,特别是GPT-3和ChatGPT在生成骨科场景的临床信件和管理计划方面的能力.
评估LLM产生的内容的可读性和准确性.

主要方法:

使用15种常见的骨科场景来提示GPT-3和ChatGPT进行临床信件和管理计划生成.
使用Flesch-Kincade等级水平,Flesch可读性易度和SMOG指数来评估可读性.
由三个独立的骨科外科医生评估生成的信件和图纸的准确性.

主要成果:

两种LLM都在最少的提示下为所有场景生成了完整的字母.
与GPT-3 (7.3/10和6.8/10) 相比,ChatGPT产生了更准确的信件 (8.7/10) 和管理计划 (7.9/10).
可读性得分表示一般可访问的内容,尽管LLM有时包含不准确的信息或遗漏.

结论:

临床信件是生成临床信件的有效工具,提供良好的可读性和准确性.
目前的LLM需要仔细审查,因为产生的内容可能存在不准确和不一致.
未来的医疗专用LLM的开发可以通过将复杂数据总结成临床信件来提高临床医生的效率.

关键词:

卫生服务管理与管理.医疗信息学医疗信息学整形外科和创伤外科手术组织发展组织发展

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