生成预训练变压器4 (GPT-4) 分析三种不同语言的医疗笔记的潜力:一项回顾性模型评估研究
Maria Clara Saad Menezes1, Alexander F Hoffmann2, Amelia L M Tan2
1Department of Biomedical Informatics, Medical School, Harvard University, Boston, MA, USA; Department of Internal Medicine, University of Texas at Southwestern, Dallas, TX, USA.
The Lancet. Digital health
|December 26, 2024
概括
像GPT-4这样的大型语言模型 (LLM) 在分析多语言医疗笔记时显示了准确性. 这项技术有可能通过临床工作流集成来改善医疗保健.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 非结构化的患者笔记对计算机分析构成挑战.
- 大型语言模型 (LLM) 具有先进的文本处理能力.
- 在分析医疗笔记时,LLM,特别是GPT-4的有效性仍未得到充分探索.
研究的目的:
- 评估GPT-4在回答医疗笔记中的预定义问题方面的表现.
- 评估GPT-4在英语,西班牙语和意大利语医疗笔记中的准确性.
- 确定LLMs在处理各种临床文档方面的潜力.
主要方法:
- 这是一项回顾性模型评估研究,涉及四个国家的八所大学医院.
- 收录了56个非标识的医疗笔记 (入院,进度,咨询) 在英语,意大利语和西班牙语.
- GPT-4处理了14个预定义问题的笔记;每个地点的两个独立医生验证了答案.
主要成果:
- 在56份医疗笔记中,GPT-4回答了784个问题.
- 医生对GPT-4的同意率为79% (622/784个响应).
- 与英语 (77%) 笔记相比,西班牙语 (88%) 和意大利语 (84%) 笔记的同意率更高.
结论:
- GPT-4在分析多种语言的医疗笔记方面表现出了显著的准确性.
- 法律学士学位显示了将其整合到临床工作流程中,以提高医疗保健的潜力.
- 进一步的研究是有必要的,以优化在临床环境中LLM应用.
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