走向后方,以确保医学中大型语言模型的风险管理
Daria Onitiu1,2, Sandra Wachter1,2, Brent Mittelstadt1
1Oxford Internet Institute, https://ror.org/052gg0110University of Oxford, United Kingdom.
概括
医疗人工智能提供商,如大型语言模型 (LLM),需要根据欧盟医疗器械条例 (MDR) 进行后行风险管理. 这确保了人工智能医疗设备的全面安全分析.
科学领域:
- 医疗器械规则 医疗器械规则
- 医疗保健中的人工智能
- 风险管理 风险管理
背景情况:
- 在医疗数据上训练的专用大型语言模型 (LLM) 越来越多地被用作AI医疗设备.
- 根据欧盟医疗器械条例 (MDR) 的当前风险管理采用"向前走"的方法.
- 这种方法可能不足以解决基于LLM的医疗器械的独特风险.
研究的目的:
- 分析专业LLM提供商如何在欧盟MDR下管理风险.
- 评估当前"前行"风险管理方法对AI医疗设备的适用性.
- 为基于LLM的医疗器械提出一个改进的风险管理框架.
主要方法:
- 审查欧盟MDR风险管理流程.
- 使用人工智能医疗设备用于肺癌检测的案例研究分析.
- "向后走"风险管理逻辑的概念化.
主要成果:
- 在MDR中的"前行"方法与AI医疗设备的预期使用要求不一致.
- 这种方法导致不完整的风险评估,忽视了新的危险和用例.
- 它未能充分解决专门的LLM固有的特定风险.
结论:
- 欧盟MDR风险管理框架要求人工智能医疗设备采用"倒行"逻辑.
- 这种向后推理的方法可以更好地识别预期的用户,危害和用例.
- 它为基于LLM的医疗器械提供了更强大的风险管理策略,确保了安全性和有效性.
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