用于开发放射学和成像学基础模型的生成人工智能:工程视角
June-Goo Lee1, Sunggu Kyung1, Namkug Kim1
1Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
Biomedical engineering letters
|January 26, 2026
概括
生成性人工智能对于推进放射学医学基础模型至关重要,因为它能够实现自我监督学习和合成数据生成. 这些人工智能模型解决了关键挑战,为可扩展和适应的医疗人工智能基础设施铺平了道路.
科学领域:
- 人工智能的人工智能是人工智能.
- 医学成像医学成像
- 基金会模型 基金会模型
背景情况:
- 放射学上的注释数据有限且异质.
- 生成型人工智能为自我监督学习和合成数据生成提供解决方案.
- 生成型人工智能解决了医疗人工智能的可扩展性,多模式对齐和数据多样性挑战.
研究的目的:
- 审查生成AI在医学基础模型中的作用.
- 探索生成模型框架和表示学习技术.
- 描述用于临床应用的多式联通大型语言模型 (MLLMs).
主要方法:
- 对生成模型 (VAE,扩散,自动回归框架) 的审查.
- 探索混合设计和表示学习 (掩盖自动编码,对比学习).
- 描述MLLM设计和培训,以整合视觉,文本和临床数据.
主要成果:
- 生成型人工智能模型构成了医疗基础模型的骨干.
- 混合设计和表示学习提高模型性能.
- 多种类型的LLM集成了各种数据,用于诸如报告生成和临床推理等应用.
结论:
- 生成型人工智能使得医疗人工智能具有可扩展性,适应性和隐私意识.
- 案例研究证明了这些模型的实际应用.
- 未来的方向包括解决幻觉,概括和可靠的AI部署的监管挑战.
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