在多种医疗模式中用于合成数据的生成人工智能:对最近的发展和挑战进行系统审查
Mahmoud Ibrahim1, Yasmina Al Khalil2, Sina Amirrajab2
1Institute of Data Science, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands; Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands; VITO, Belgium.
Computers in biology and medicine
|March 1, 2025
概括
本综述探讨了生成模型,如GAN,VAE,DM和LLM,用于合成各种医疗数据. 关键发现突出了个性化,临床整合和对合成医疗数据的标准化评估方面的差距.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数据合成数据合成
背景情况:
- 生成模型 (GAN,VAE,DM,LLM) 越来越多地用于医疗数据合成.
- 以前的审查往往只关注特定的模型或数据类型.
- 需要进行广泛的多模式审查,以确定跨领域的见解和机会.
研究的目的:
- 系统地审查用于合成各种医疗数据 (成像,文本,时间序列,EHR) 的生成模型.
- 分析合成应用,生成技术和评估方法.
- 确定医疗研究中生成模型的当前差距和未来方向.
主要方法:
- 在Scopus,PubMed和ArXiv的系统文献搜索 (2021年1月 - 2023年11月).
- 包括最近的工作,重点关注超越GAN和条件生成的进步.
- 围绕合成应用,生成技术和评估方法构建的分析.
主要成果:
- 生成模型可以合成各种医疗数据类型,但合成方法往往缺乏患者背景和临床整合.
- 条件生成和多式联机合成尚未得到充分探索,但具有前景.
- 标准化基准,大规模验证和对合成医疗数据的隐私意识评估框架存在重大差距.
结论:
- 在医疗数据合成中,需要更个性化,临床综合和跨模式的生成方法.
- 标准化,临床相关性和隐私意识的评估框架至关重要.
- 基准测试和比较研究对于促进合作和推进该领域至关重要.
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