大型语言模型时代的数字健康自然语言处理
Abeed Sarker1, Rui Zhang2, Yanshan Wang3
1Emory University, Atlanta, GA, USA.
Yearbook of medical informatics
|April 8, 2025
概括
大型语言模型 (LLM) 正在改变医疗保健的自然语言处理 (NLP). 研究表明,医疗文本的定制LLM趋势趋势,但诸如偏见和公平使用之类的伦理考虑需要更多的关注.
科学领域:
- 生物医学自然语言处理 (NLP)
- 医疗保健中的人工智能
背景情况:
- 大型语言模型 (LLM) 正在迅速推进NLP能力.
- 医疗保健行业正在探索LLM的各种应用.
研究的目的:
- 综合当前关于医学NLP中LLM的研究.
- 总结LLMs在医疗保健中的进展和应用.
主要方法:
- 进行了对生物医学NLP中LLM最近研究的选择性文献审查.
- 来自PubMed,ACL Anthology,IEEE Explore和Google Scholar的文献来源. 这是一个非常好的例子.
- 分析了针对医学文本和各种数据源 (医学文献,电子健康记录,社交媒体) 定制的LLM.
主要成果:
- 一般目的的LLM很受欢迎,但针对生物医学任务的定制开源LLM正在增加.
- 在EHR和生物医学文献中的应用比社交媒体更为突出.
- 传统模型在分类/NER方面表现优于LLM;LLM在少数镜头和生成任务方面表现出色.
- 关于LLM评估,偏见,隐私和公平性的研究是有限的.
结论:
- 在医学NLP任务方面,LLM具有显著的潜力,可以彻底改变NLP任务.
- 未来的研究必须优先考虑以任务为导向的评估,偏见缓解和公平的LLM在医疗保健中的部署.
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