大型语言模型和合成健康数据:进展和前景
Daniel Smolyak1, Margrét V Bjarnadóttir2, Kenyon Crowley3
1Department of Computer Science, University of Maryland, College Park, College Park, MD 20742, United States.
JAMIA open
|October 28, 2024
概括
大型语言模型 (LLM) 为生成高质量的合成健康数据提供了新的机会和风险. 需要进一步的研究来探索LLM在合成健康数据生成 (SHDG) 中的潜力和挑战.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 高质量的合成健康数据对于高级分析至关重要,因为获得真实健康数据存在挑战.
- 对临床发现,预测和运营卓越的日益增长的需求需要可靠的合成数据解决方案.
研究的目的:
- 探索合成健康数据生成 (SHDG) 中大语言模型 (LLM) 的潜力和风险.
- 总结目前的SHDG方法格局,并确定LLM如何应对现有挑战.
主要方法:
- 对SHDG领域进行系统范围审查.
- 对SHDG应用的最新LLM方法的分析.
- 研究LLM的能力和局限性.
主要成果:
- 目前的生成模型,如生成对抗网络 (GAN),在SHDG中面临限制.
- 在SHDG中,LLM提供了有希望的方法来缓解现有的挑战.
- 确定了SHDG中LLM的六个主要研究方向:评估,采用,效率,概括,公平和监管.
结论:
- 在健康领域,LLM显示出巨大的潜力,但也带来了风险.
- 进一步研究LLM对SHDG的优缺点是必不可少的.
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