大型语言模型在从组织病理学报告中提取非结构化数据方面的能力
Sarah Adamson1,2, Christopher Berry3, Nikki R Adler1
1School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia.
The Australasian journal of dermatology
|December 31, 2025
概括
大型语言模型 (LLM) 为手动医疗数据提取提供了更快,更便宜,更准确的替代方案. 虽然幻觉和隐私等挑战仍然存在,但LLM承诺将彻底改变研究和患者监测.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 手动提取医疗数据是耗时的,劳动密集的,昂贵的,容易出现错误的.
- 以前的自动化方法需要技术专业知识,缺乏可靠的准确性.
研究的目的:
- 审查大型语言模型 (LLM) 在医学数据收集中的应用.
- 在准确性,速度,成本和错误类型方面评估LLM性能.
- 确定在医疗保健中实施LLM的挑战和未来方向.
主要方法:
- 探索医学数据提取的LLM能力.
- 分析数据类型,LLM架构,培训要求和输出格式.
- 审查常见的错误,安全问题和潜在的解决方案.
主要成果:
- 在医学数据提取方面,LLM显示出了节省时间和成本的巨大潜力.
- 与传统方法相比,LLM提供了更好的准确性和效率.
- 关键的挑战包括减少幻觉,确保患者隐私和处理复杂的数据格式.
结论:
- 在有效的医学研究和实时患者结果监测方面,LLM是一个有前途的进步.
- 克服与准确性,隐私和可用性相关的挑战对于广泛采用至关重要.
- 医疗保健专业人员需要培训,以便在自动数据提取中有效利用LLM.
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