医学成像中的AI和机器学习:从开发到翻译的关键点
Ravi K Samala1, Karen Drukker2, Amita Shukla-Dave3,4
1Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States.
BJR artificial intelligence
|June 3, 2024
概括
在医学成像中推进人工智能 (AI) 需要处理数据,算法和性能评估. 克服人工智能开发和临床整合方面的挑战对于放射学进步至关重要.
科学领域:
- 医疗成像医学成像
- 人工智能 (AI) 是一种人工智能.
- 机器学习 (ML) 是指机器学习.
背景情况:
- 医学成像中的AI/ML创新需要大量的数据,先进的算法和彻底的性能评估.
- 关键的评估领域包括概括性,不确定性,偏见,公平性,可信度和可解释性.
研究的目的:
- 解决医疗成像中AI/ML技术的开发和采用的关键障碍.
- 通过解决复杂的临床翻译挑战,探索在放射学中推进人工智能的机会.
主要方法:
- 评论解决AI/ML模型设计,开发和绩效评估中的多方面的挑战.
- 讨论利益相关者的参与,成本效益,监管合规性以及现实世界的绩效反循环.
主要成果:
- 人工智能/ML在临床任务中的广泛整合在模型设计和性能评估方面面临重大挑战.
- 解决微妙但关键因素对于克服采用障碍至关重要.
结论:
- 坚定承诺克服AI/ML开发和绩效评估方面的问题,对于临床整合至关重要.
- 对这些因素的全面关注将推动人工智能驱动放射学的新机遇和进步.
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