医学成像中的生成人工智能:基础,进展和临床翻译
Shanshan Wang1, Xuanru Zhou1,2, Cheng Li1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Research (Washington, D.C.)
|December 17, 2025
概括
生成型人工智能 (AI) 正在通过GAN和扩散模型等先进技术彻底改变医学成像. 本综述综合了这些人工智能应用,解决了挑战,并提出了临床整合的评估框架.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 包括GAN,VAE和扩散模型在内的生成AI正在改变医学成像能力.
- 应用范围包括数据合成,图像增强,模式翻译和时空建模.
- 生成型模型解决了数据稀缺性和跨模式整合等挑战.
研究的目的:
- 为医疗成像生成人工智能的最新进展提供全面的审查.
- 评估生成AI在临床成像工作流程中的不断扩大的角色.
- 提出一个评估框架,并确定部署挑战.
主要方法:
- 对生成建模技术 (GAN,VAE,扩散模型,基础架构) 的系统审查.
- 检查人工智能在成像采集,重建,合成,诊断,规划和预后方面的应用.
- 提出一个三级评估框架 (像素,功能,任务级别).
主要成果:
- 生成型人工智能增强了医学成像工作流程的各个阶段,从获取到预后.
- 该审查确定了包括概括,数据隐私和监管障碍在内的关键障碍.
- 与基础模型的融合承诺可扩展和集成的成像系统.
结论:
- 生成型人工智能为推进医学成像技术提供了巨大的潜力,解决了关键的挑战.
- 一个强大的评估框架对于临床翻译和确保可靠性至关重要.
- 未来的研究应该集中在克服部署障碍和促进跨学科合作上.
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