规范放射学实践中的生成人工智能:一种三层式方法,以平衡风险与创新.
Vrushab Gowda1, Bernardo C Bizzo2, Keith J Dreyer2
1Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.G., B.C.B., K.J.D.); Harvard Medical School, Boston, Massachusetts (V.G., B.C.B., K.J.D.).
Academic radiology
|June 5, 2025
概括
放射学领域的生成人工智能 (AI) 需要一个强大的治理框架. 拟议的三层模型平衡了创新与安全,为放射科医生和患者管理风险.
科学领域:
- 放射学 放射学是指放射学
- 人工智能的人工智能
- 医学治理 医学治理
背景情况:
- 生成型人工智能工具正在迅速进入放射学实践环境.
- 现有的监管途径难以应对这些通用技术带来的独特挑战.
- 有关责任,隐私和与生成人工智能相关的临床风险存在担忧.
研究的目的:
- 为放射学领域的生成人工智能提出一个全面的治理模型.
- 解决对这些快速发展的技术的监督方面的差距.
- 为放射科医生提供风险管理策略.
主要方法:
- 开发一个三层治理模型.
- 综合联邦法规,机构指导方针和行业自我监管框架.
- 对现有的FDA市场前审查限制的分析.
主要成果:
- 拟议的三层治理模型为人工智能监督提供了多层次的方法.
- 这种模式将联邦法规作为基础架构整合在一起.
- 机构指导和行业自我监管的额外层次创造了一个全面的系统.
结论:
- 三层级治理模型为放射学中生成性AI提供了有效的风险管理策略.
- 该框架旨在促进技术的持续发展,同时确保患者的安全.
- 实施这种模式促进了负责任的创新,并提高了患者的护理.
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