Regulatory responses to medical machine learning.

Regulatory responses to medical machine learning.
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DOI:
10.1093/jlb/lsaa002
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发表时间:
2020-01
影响因子:
3.4
通讯作者:
Cohen G
Cohen G
中科院分区:
医学3区
文献类型:
--
作者:
Minssen T;Gerke S;Aboy M;Price N;Cohen G

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公司和医疗保健提供商正在开发和实施医疗人工智能的新应用,包括医疗机器学习(MML)的人工智能子类型。MML基于机器学习(ML)算法的应用,可自动识别模式并根据医疗数据采取行动,以指导临床决策。MML带来了挑战并提出了重要问题,包括(1)监管机构将如何评估基于MML的医疗器械以确保其安全性和有效性?(2)在国际背景下应该考虑哪些额外的MML考虑因素?为了解决这些问题,我们分析了美国和欧洲目前对MML的监管方法。然后,我们研究了国际视角和更广泛的影响,讨论了数据隐私、出口、解释、训练集偏差、上下文偏差和商业保密等考虑因素。
Companies and healthcare providers are developing and implementing new applications of medical artificial intelligence, including the artificial intelligence sub-type of medical machine learning (MML). MML is based on the application of machine learning (ML) algorithms to automatically identify patterns and act on medical data to guide clinical decisions. MML poses challenges and raises important questions, including (1) How will regulators evaluate MML-based medical devices to ensure their safety and effectiveness? and (2) What additional MML considerations should be taken into account in the international context? To address these questions, we analyze the current regulatory approaches to MML in the USA and Europe. We then examine international perspectives and broader implications, discussing considerations such as data privacy, exportation, explanation, training set bias, contextual bias, and trade secrecy.
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