Algorithm Change Protocols in the Regulation of Adaptive Machine Learning-Based Medical Devices.
Algorithm Change Protocols in the Regulation of Adaptive Machine Learning-Based Medical Devices.
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DOI：
10.2196/30545
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发表时间：
2021-10-26
影响因子：
7.4
通讯作者：
Starlinger J
中科院分区：
文献类型：
--
作者：
Gilbert S;Fenech M;Hirsch M;Upadhyay S;Biasiucci A;Starlinger J
人工智能(AI)和机器学习(ML)方法在医疗保健领域的最大优势之一是，它们的性能可以根据来自数据的自动学习的更新而不断提高。然而，医疗保健ML模型目前基本上是根据为更新缓慢的医疗设备的早期时代开发的条款进行监管的-需要对ML算法生成的模型的每一次重大更新进行重大文档重塑和重新验证。这对只会偶尔重新训练和更新的模型会产生小问题，但对将实时或接近实时地从数据中学习的模型会产生大问题。监管机构已经宣布了从根本上改变监管方式的行动计划。在这一点上，我们审查了这一领域的当前监管框架和发展。回顾了现状和最近的发展，我们认为这些创新的医疗保健方法需要与之相匹配的创新监管方法，这些方法将为患者带来好处。来自世界卫生组织的国际视角，以及食品和药物管理局提出的方法，基于对工具开发商的质量管理系统和定义的算法更改协议的监督，提供了亟需的范式转变，并努力采取平衡的方法，通过人工智能创新实现医疗保健的快速改善，同时确保患者安全。欧盟(EU)监管框架草案表明了类似的做法，但尚未提供关于算法更改协议将如何在欧盟实施的细节。我们认为必须提供细节，我们描述了如何以一种方式来实现基于AI/ML的创新对欧盟患者和医疗保健系统的全部好处。
One of the greatest strengths of artificial intelligence (AI) and machine learning (ML) approaches in health care is that their performance can be continually improved based on updates from automated learning from data. However, health care ML models are currently essentially regulated under provisions that were developed for an earlier age of slowly updated medical devices—requiring major documentation reshape and revalidation with every major update of the model generated by the ML algorithm. This creates minor problems for models that will be retrained and updated only occasionally, but major problems for models that will learn from data in real time or near real time. Regulators have announced action plans for fundamental changes in regulatory approaches. In this Viewpoint, we examine the current regulatory frameworks and developments in this domain. The status quo and recent developments are reviewed, and we argue that these innovative approaches to health care need matching innovative approaches to regulation and that these approaches will bring benefits for patients. International perspectives from the World Health Organization, and the Food and Drug Administration’s proposed approach, based around oversight of tool developers’ quality management systems and defined algorithm change protocols, offer a much-needed paradigm shift, and strive for a balanced approach to enabling rapid improvements in health care through AI innovation while simultaneously ensuring patient safety. The draft European Union (EU) regulatory framework indicates similar approaches, but no detail has yet been provided on how algorithm change protocols will be implemented in the EU. We argue that detail must be provided, and we describe how this could be done in a manner that would allow the full benefits of AI/ML-based innovation for EU patients and health care systems to be realized.
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影响因子：
7.4
作者：
Higgins, David;Madai, Vince I.
通讯作者：
Madai, Vince I.
影响因子：
3.4
作者：
Minssen T;Gerke S;Aboy M;Price N;Cohen G
通讯作者：
Cohen G
影响因子：
39
作者：
Wong, Andrew;Otles, Erkin;Singh, Karandeep
通讯作者：
Singh, Karandeep
影响因子：
4.1
作者：
Lyell D;Coiera E;Chen J;Shah P;Magrabi F
通讯作者：
Magrabi F
DOI：
10.1093/jamia/ocaa319
发表时间：
2021-01-15
影响因子：
6.4
作者：
Petersen, Carolyn;Smith, Jeffery;Wright, Adam
通讯作者：
Wright, Adam