弥合业绩差距:系统地优化日本医疗PHI提取本地LLMs的系统优化
Akihiko Wada1, Mitsuo Nishizawa2,3, Akira Yamamoto3
1Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan. a-wada@juntendo.ac.jp.
Scientific reports
|January 21, 2026
概括
优化本地大型语言模型 (LLM) 显著提高了它们在医学文本上的性能,实现了接近基于云的AI功能. 这一框架使医疗保健中安全,高效地采用人工智能,而不会损害患者的隐私.
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:
- 基于云的大型语言模型 (LLM) 提供先进的医疗文本处理,但由于受保护健康信息 (PHI) 传输限制,面临隐私挑战.
- 医疗机构通常需要数据主权,限制外部AI服务的使用,尽管有潜在的合同协议,如商业合作伙伴协议 (BAA).
- 当地LLM提供数据隐私,但与基于云的对应产品相比,其性能历来一直落后.
研究的目的:
- 引入一个新的五阶段优化框架,以提高医疗文本处理本地LLM的性能.
- 用合成日本放射学报告将本地LLM与云领导者进行基准测试.
- 调查绩效模式,并确定当地LLMs的潜在优化门.
主要方法:
- 开发和应用当地LLMs的五阶段优化框架.
- 基准测量14个本地LLM与基于云的模型,使用160个合成日本放射学报告.
- 绩效分析,包括对得分改善和处理时间的统计评估.
主要成果:
- 在基线得分低于87-88的本地LLM中观察到显著的绩效增长 (+6.92分,p < 0.001),这表明潜在的优化值.
- 优化的Mistral-Small-3.2模型获得了91.54分,达到GPT-4.1性能的97.8%.
- 优化的模型证明了完美的规则遵守和临床上可接受的处理时间每报告24.6秒批量工作流程.
结论:
- 系统地优化本地LLM可以实现与领先的基于云的模型可比的性能.
- 拟议的优化框架指导医疗机构在战略性部署人工智能时,平衡性能,效率和患者隐私.
- 这种方法通过确保数据主权和遵守隐私法规,使可信的人工智能采用成为可能.
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