大型语言模型在罕见疾病诊断中表现一致,涉及10种语言和4917例病例
Leonardo Chimirri1, J Harry Caufield2, Yasemin Bridges3
1Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
EBioMedicine
|October 15, 2025
概括
大型语言模型 (LLM) 在罕见疾病的十种语言中显示出差异诊断的一致性能. 这表明,LLM可以在全球范围内支持临床决策,克服医疗保健中的语言障碍.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 基因组学就是基因组学.
背景情况:
- 大型语言模型 (LLM) 越来越多地用于医疗应用,包括差异诊断.
- 目前的LLM主要以英语文本进行培训,这限制了其全球适用性.
- 需要对非英语语言的LLM绩效进行大规模评估,以诊断罕见疾病.
研究的目的:
- 评估跨多种语言的LLMs的差异诊断性能.
- 为了比较GPT-4o和Meditron3-70B在诊断罕见疾病中的性能.
- 在全球范围内评估语言对LLM诊断准确性的影响.
主要方法:
- 通过使用人类表型本体学 (HPO) 术语和Phenopacket Schema生成了4917个临床细节.
- 在10种语言 (英语,中文,捷克语,荷兰语,法语,德语,意大利语,日语,西班牙语,土耳其语) 使用翻译的HPO术语创建提示.
- 应用了GPT-4o和Meditron3-70B,使用零射击提示进行差异诊断,以本体学为基础的评估.
主要成果:
- 在英语病例中,GPT-4o在27.0%的病例中获得了前三名的诊断,在非英语病例中获得了25.4%-28.6%.
- 在英语病例中,Meditron3-70B在20.9%的病例中获得了前三名的诊断,在非英语病例中获得了19.9%-24.0%的诊断.
- 两种模型的十种测试语言的差异诊断性能在很大程度上是一致的.
结论:
- 在罕见疾病的差异诊断中,LLM的表现在英语和其他9种语言中是一致的.
- 在非英语临床环境中,LLM显示出潜在的实用性,这表明它具有更广泛的全球适用性.
- 这项研究支持在英语地区以外扩展基于LLM的诊断工具.
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