确保智能医疗设备:人工智能保证案例
Anita Khadka1, Gregory Epiphaniou1, Carsten Maple1
1University of Warwick, Coventry, UK.
Studies in health technology and informatics
|July 1, 2025
概括
人工智能 (AI) 和机器学习 (ML) 增强了医疗设备,但也带来了挑战. 提出了结构化保证案例模式,以通过解决数据完整性和监管合规性等风险,确保可靠的AI支持医疗系统.
科学领域:
- 医疗设备工程 医疗设备工程
- 医疗保健中的人工智能
- 机器学习用于医疗应用.
背景情况:
- 在医疗设备中整合AI和ML可以在自动化,精度和效率方面取得重大进展.
- 这些技术带来了复杂的挑战,包括数据完整性,算法透明度,对抗性强度和监管合规性.
- 现有的保证方法对于人工智能驱动的医疗系统的动态性质是不够的.
研究的目的:
- 解决AI/ML在医疗器械中的挑战.
- 提出专门为支持人工智能的医疗设备设计的结构化保证案例模式.
- 提高智能医疗系统的可信度.
主要方法:
- 讨论各种结构化的保证案例模式.
- 探索与基于ML的医疗系统相关的潜在风险.
- 设计保证案例以减轻已识别的风险.
主要成果:
- 确定AI/ML医疗器械保证的关键挑战.
- 为支持人工智能的设备开发量身定制的保证案例模式.
- 构建可靠的人工智能驱动医疗系统的框架.
结论:
- 结构化保证案例对于验证医疗器械中的AI/ML至关重要.
- 解决数据完整性和可靠性等风险对于监管合规至关重要.
- 提出的模式有助于开发可靠和值得信赖的智能医疗系统.
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