Ada Health GmbH, Berlin, Germany.
Else Kröner-Fresenius Center for Digital Health, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
J Med Internet Res. 2021 Oct 26;23(10):e30545. doi: 10.2196/30545.
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.
人工智能 (AI) 和机器学习 (ML) 在医疗保健中的最大优势之一是，它们的性能可以根据从数据中自动学习的更新不断改进。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制定的规定进行监管的——这需要对模型进行重大的文档重塑和重新验证，而这些模型是由 ML 算法生成的。这对于偶尔重新训练和更新的模型来说只是小问题，但对于那些将实时或接近实时从数据中学习的模型来说则是大问题。监管机构已经宣布了对监管方法进行根本性变革的行动计划。在这篇观点文章中，我们考察了这一领域的现行监管框架和发展情况。审查了现状和最新进展，并认为这些针对医疗保健的创新方法需要与针对监管的创新方法相匹配，这些方法将为患者带来益处。世界卫生组织（WHO）和美国食品和药物管理局（FDA）的提议方法提供了国际视角，这些方法围绕着对工具开发者质量管理体系的监督和定义算法变更协议展开，提供了急需的范式转变，并努力在通过人工智能创新实现医疗保健快速改进的同时，确保患者安全。欧盟（EU）的监管框架草案表明了类似的方法，但尚未就如何在欧盟实施算法变更协议提供细节。我们认为必须提供详细信息，并描述如何以一种允许欧盟患者和医疗保健系统充分受益于 AI/ML 创新的方式来实现这一点。
