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评估大型语言模型的医学,外语和自我报告的遗传病症描述
Kendall A Flaharty1, Ping Hu1, Suzanna Ledgister Hanchard1
1Medical Genomics Unit, National Human Genome Research Institute, National Institutes of Health, 10 Center Dr, Bethesda, MD 20892, USA.
American journal of human genetics
|August 15, 2024
概括
大型语言模型 (LLM) 在医学上表现有前途,但像ChatGPT-4这样的封闭源代码模型在识别遗传条件方面明显优于开源选项. 性能因模型大小和提示符类型而异.
科学领域:
- 人工智能在医学中的应用
- 医疗保健的自然语言处理.
- 基因组医学和诊断 基因组医学和诊断
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于医学应用,包括从临床笔记中生成差异诊断.
- 关于开源与封闭源LLM的比较性能以及它们在医疗与非专业人士查询中的准确性仍然存在关键问题.
- 评估识别遗传疾病的LLM能力对于其安全有效地融入临床工作流程至关重要.
研究的目的:
- 评估各种开源和封闭源LLM在从临床医生和非专业人士提示中识别遗传疾病方面的表现.
- 将LLM的表现与谷歌搜索和Phenomizer等传统方法进行比较.
- 调查提示工程技术 (上下文提示,关键字删除) 和数据特征 (教科书与现实世界描述) 对LLM准确性的影响.
主要方法:
- 评价了7个LLM工具 (Llama-2-chat,Vicuna,Medllama2,Bard/Gemini,Claude,ChatGPT3.5,ChatGPT-4) 和2个非LLM工具 (谷歌搜索,Phenomizer).这些工具包括:
- 使用教科书般的临床医生问题和63种遗传疾病的非专业翻译.
- 分析了列表类型和定义类型的上下文提示,关键字删除和真正的个人描述对LLM准确性的影响.
主要成果:
- 封闭源代码LLM的表现优于开源LLM;ChatGPT-4获得了最高的准确性 (89%-90%).
- 对于开源的LLM,准确性随着模型大小的增加而增加 (7b: 21%-49%, 13b: 41%-51%, >33b: 54%-68%).
- 在临床医生和非专业人士对3个LLM和谷歌搜索的提示之间存在显著的绩效差距. 列表类提示提高了性能,而定义类提示没有. 对于大多数LLM来说,稀有术语删除降低了准确性,现实世界的描述产生了低准确性 (最大21%).
结论:
- 与开源替代品相比,封闭源LLM,特别是ChatGPT-4,在识别遗传疾病方面表现出更好的能力.
- 模型大小和提示工程策略显著影响LLM在此诊断任务中的性能.
- 需要进一步的研究来提高LLMs的准确性和可靠性,以便用于现实世界的临床应用,特别是针对患者的特定数据.
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