在低收入和中等收入国家为母亲和新生儿保健提供大型语言模型
Lauren Yu1, Gary L Darmstadt2, Victoria Ward2
1Department of Computer Science, Stanford University, Palo Alto, CA, USA.
The Journal of pediatrics
|February 15, 2026
概括
大型语言模型 (LLMs) 显示,在低收入和中等收入国家 (LMICs) 协助孕产妇和新生儿保健方面存在局限性. GPT-4o产生了不准确的数据,表现出偏见,并未正确引用来源,表明需要对上下文化数据进行微调.
科学领域:
- 医疗保健中的人工智能
- 全球母亲和新生儿健康
- 医疗信息学 医疗信息学
背景情况:
- 低收入和中等收入国家 (LMICs) 在产妇和新生儿医疗保健方面面临重大挑战.
- 大型语言模型 (LLM) 为信息传播和决策支持提供了潜力.
研究的目的:
- 调查LLM,特别是GPT-4o在LMICs中支持孕产妇和新生儿医疗保健的有效性.
- 评估GPT-4o在生成与LMIC相关的健康信息方面的准确性.
主要方法:
- 评估了GPT-4o在四个领域中产生准确答案的能力:医疗病例中的疾病患病率,数据稀缺地区的患病率数据,标准化医学检查问题和主观健康查询.
- 利用了来自6个世卫组织地区和204个国家的公开数据.
- 用重复提示来确保LLM的答案的统计学意义.
主要成果:
- GPT-4o产生了医疗病例,这些病例并不准确地反映了全球疾病的流行情况,过度代表了美洲.
- 该LLM表现出偏见,在主观健康主题中为高婴儿死亡率的国家分配较低的排名.
- 在44%的案例中,GPT-4o提供了无法获得的怀孕统计数据,而不承认不确定性,其46.7%的来源引用是错误的.
- 虽然GPT-4o正确地回答了79%的一般妊娠医学检查问题,但在考虑区域健康问题和症状时,它犯了错误.
结论:
- 像GPT-4o这样的通用LLM在LMICs指导孕产妇和新生儿保健方面存在重大限制.
- 调查结果强调需要微调LLM与特定背景数据,以提高准确性和减少偏差.
- 需要进一步的研究来开发针对LMIC医疗保健环境的定制LLM解决方案.
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