使用大型语言模型从外科病理学报告中自动提取数据报告:回顾性队列研究
Denise Lee1, Akhil Vaid2, Kartikeya M Menon1
1Department of Surgery, Icahn School of Medicine at Mount Sinai, 10 Union Square East, Suite 2L, New York, NY, 10003, United States, 1 212 241 2891.
JMIR formative research
|April 7, 2025
概括
一个本地部署的大型语言模型 (LLM) 证明了从外科病理报告中回答医疗问题 (MQA) 的显著时间节省,达到89%的准确性. 进一步优化可以增强临床数据提取.
科学领域:
- 临床自然语言处理 (NLP)
- 医疗保健中的人工智能
背景情况:
- 大型语言模型 (LLM) 为临床NLP任务提供了潜力,例如医疗问题答案 (MQA).
- 对成本,计算能力和患者隐私的担忧限制了LLM在医疗保健中的采用.
- 在机构防火墙中部署的开源LLM可以减轻隐私风险.
研究的目的:
- 从外科病理学报告中评估本地部署的LLM对自动化MQA的性能.
- 在提取临床信息方面,比较LLM与人类审稿人的准确性和效率.
主要方法:
- 84份甲状腺癌外科病理报告由两个人体审查员和FastChat-T5 LLM分析.
- 报告被细分,转换为嵌入式,并为LLM处理进行上下文集成.
- 提出了12个医学问题,以提取分期和复发风险数据,并评估了响应时间和一致性.
主要成果:
- 人类审查者实现了99%的一致性.
- 该LLM与人类审稿人达到了89%的一致性.
- 与人类审稿人 (170.7分钟和115分钟) 相比,LLM的反应明显更快 (19.56分钟).
结论:
- 在当地部署的LLM为MQA在临床环境中以可接受的准确性提供了相当大的时间节省.
- 快速工程和微调可以进一步改进从临床叙述中自动提取数据.
- 在保证隐私的情况下,LLM对实时临床洞察有希望.
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