放射学基础模型:什么,如何,为什么,以及为什么不
Magdalini Paschali1, Zhihong Chen1, Louis Blankemeier1
1From the Stanford Center for Artificial Intelligence in Medicine and Imaging, 1701 Page Mill Rd, Palo Alto, CA 94304 (M.P., Z.C., L.B., M.V., A.Y., C.B., C.L., S.G., A.C.); Departments of Radiology (M.P., Z.C., A.Y., C.L., S.G., A.C.), Electrical Engineering (L.B.), Computer Science (M.V.), Medicine (C.L.), and Biomedical Data Science (C.L., A.C.), Stanford University, Stanford, Calif; and Department of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (C.B.).
基础模型 (FMs) 是一种先进的AI,能够解释文本和图像. 本综述指导放射科医生培训放射学特定的FM,涵盖数据,培训,能力和安全,负责任的使用评估.
科学领域:
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
- 医疗成像医学成像
背景情况:
- 大规模的深度学习模型,称为基础模型 (FMs),擅长解释和生成文本和图像数据.
- FMs在大量未标记的数据集上接受培训,在各种任务中表现出高绩效,并引起了大量的关注.
- 射线学中FM的变革潜力需要使放射科医生对其培训和应用的认识.
研究的目的:
- 为放射科医生提供放射学背景下对FM的基本理解.
- 阐明培训放射学专用FM的要求,包括数据,范式,能力和评估.
- 弥合FM技术进步与安全和负责任实施的临床需求之间的差距.
主要方法:
- 审查与放射学中的FMs相关的基本概念和术语.
- 专注讨论了培训放射学专用FM的基本组成部分:数据要求,培训范式,模型能力和评估策略.
- 技术进步与负责任的FM开发临床要求的综合.
主要成果:
- 详细解释为放射学受众量身定制的基础模型概念.
- 确定培训放射学专用FM的关键考虑因素,包括数据策划,算法方法,绩效评估和道德影响.
- 为使人工智能开发与临床实践保持一致,以确保患者受益的框架.
结论:
- 放射科医生需要了解基础模型,以利用其在医学成像方面的潜力.
- 培训放射学专用FM的综合方法至关重要,平衡技术能力与临床实用性和安全性.
- 负责任地开发和部署放射学中的FM有望提高患者护理和提供者效率.
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