放射学中的生成AI和基础模型:应用,机遇和潜在挑战
Neda Tavakoli1, Zahra Shakeri2, Vrushab Gowda3
1Department of Radiology, Northwestern Memorial Hospital, 676 N Saint Clair St, Arkes Family Pavilion Ste 800, Chicago, IL 60611.
Radiology
|November 18, 2025
概括
基础模型 (FMs) 正在推动医疗成像中的AI,为数据稀缺的设置提供适应性. 偏见和成本等挑战阻碍了临床整合,需要合作解决方案来实现负责任的部署.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 放射学 放射学是一门学科.
背景情况:
- 基础模型 (FMs) 使用自我注意机制进行多式联络数据处理.
- FMs可以通过转移学习,微调和少数镜头学习来适应特定的医学成像任务,使用较小的数据集.
- 在FM中的生成AI能力有助于创建合成医疗图像,解决注释限制.
研究的目的:
- 审查FM和生成AI在放射学中不断变化的作用.
- 突出最近的研究进展和医疗成像中FMs的临床应用.
- 确定FMS负责任的临床整合的关键挑战.
主要方法:
- 对医学成像中的Foundation模型的当前文献的综述.
- 分析各种成像方式在放射学中的应用.
- 探索适应技术,如转移学习和快速工程.
- 检查用于合成数据创建的生成AI.
主要成果:
- 射线仪显示出提高诊断准确度和简化放射学工作流程的潜力.
- 适应技术使得FM在数据稀缺的医学成像场景中具有价值.
- 生成型人工智能有助于克服数据注释的限制.
结论:
- 放射学中FM的临床整合面临诸多挑战,包括解释性,偏见,隐私,法规和计算成本.
- 解决这些障碍需要技术开发人员,医疗保健提供者和监管机构之间的合作.
- 在放射学中负责任地部署FM需要克服已识别的挑战.
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