聊天中的质量,准确性和偏差 基于GPT的医学摘要总结
Joel Hake1, Miles Crowley1, Allison Coy1
1Department of Family Medicine and Community Health, University of Kansas Medical Center, Kansas City, Kansas.
Annals of family medicine
|March 25, 2024
概括
像ChatGPT这样的大型语言模型可以有效地总结医学研究,提供高质量和准确性. 虽然有助于加快文献审查,但关键的医疗决策仍然需要完整的文章评估.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 由于时间有限,医生面临着跟上快速扩展的临床知识的挑战.
- 像ChatGPT这样的大型语言模型 (LLM) 显示了总结和优先考虑研究的潜力.
- 对LLM的一个关键问题是他们倾向于产生不准确的信息 ("幻觉").
研究的目的:
- 评估ChatGPT总结同行评审的医学摘要的能力.
- 评估ChatGPT生成的摘要的质量,准确性和偏见.
- 为了比较医生和ChatGPT医学文章和期刊的相关性评级.
主要方法:
- 来自14个期刊的140篇同行评审摘要被ChatGPT总结.
- 医生们对生成的摘要的质量,准确性和偏见进行了评价.
- 人类相关性评级与ChatGPT对期刊和个人文章的相关性分类进行了比较.
主要成果:
- 聊天GPT摘要比原始摘要短70% (平均为2,438至739个字符).
- 摘要在质量 (中位数为90/100),准确性 (中位数为92.5/100) 和低偏差 (中位数为0/100) 方面获得了高评分.
- 严重的不准确不常见;期刊相关性分类反映了医生评级,但个人文章相关性分类是温和的.
结论:
- 聊天GPT有效地生成医学文献的简洁,高质量,准确和低偏见的摘要.
- 像ChatGPT这样的LLM可以帮助医生加快科学文献审查.
- 医生应该使用全文评估来做出关键的医疗决策,并支持像pyJournal.Watch这样的工具.
关键词:
聊天GPT 聊天GPT 聊天人工智能的人工智能是人工智能.偏见 偏见 偏见 偏见 偏见对科学文献进行批判性评估.大型语言模型.初级保健研究初级保健研究文本分析 文本分析文本采矿 文本采矿是什么更多相关视频
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