放射性图像分析的大规模基础模型:临床应用,技术挑战和未来方向
Yashbir Singh1, Orhan Unal2, Farzana Ali3
1Mayo Clinic, Rochester, MN, USA. Singh.yashbir@mayo.edu.
Journal of imaging informatics in medicine
|January 13, 2026
概括
本综述为在临床放射学中实施基础模型提供了实际指导,重点关注现实世界的部署和整合策略,以改善患者护理.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医学成像信息学 医疗成像信息学
背景情况:
- 基础模型为放射性图像分析提供了多功能AI,超越了特定任务的方法.
- 在将人工智能研究转化为可持续的临床放射学实践方面存在差距.
研究的目的:
- 为了弥合基础模型研究和临床放射学实施之间的差距.
- 为在放射科部门采用人工智能提供可行的,基于证据的指导.
主要方法:
- 审查子专业特定的部署策略和现实世界的绩效基准.
- 来自15个医疗保健系统的PACS/RIS整合协议的分析.
- 评估基础模型架构,预培训和适应技术.
主要成果:
- 基金会模型显示了病变检测,疾病分类和跨模式的自动报告方面的进展.
- 实施挑战包括临床验证,监管批准,数据异质性,可解释性和计算效率.
- 成功的集成需要关注完整的数据生命周期和诸如边缘计算之类的实际解决方案.
结论:
- 基金会模型对改变放射学具有重大前景.
- 为放射科医生,管理人员和信息学专家提供了可操作的指导,以有效实施.
- 未来的方向包括下一代架构,个性化医疗和增强的可访问性.
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