Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device.
Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device.
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以技术为基础，循证驱动和以患者为中心的：将软件作为医疗设备进行调节的前进之路。
DOI：
10.2196/34038
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发表时间：
2022-01-27
影响因子：
3.2
通讯作者：
Banerjee A
中科院分区：
文献类型：
--
作者：
Carolan JE;McGonigle J;Dennis A;Lorgelly P;Banerjee A
人工智能(AI)是一门广泛的学科，旨在理解和设计显示智能特性的系统。机器学习(ML)是人工智能的一个子集，描述了算法和模型如何帮助计算机系统逐步提高其性能。在医疗保健中，AI/ML的一个日益常见的应用是软件即医疗设备(SaMD)，其目的是诊断、治疗、治愈、缓解或预防疾病。AI/ML包括“锁定”或“持续学习”算法。锁定算法始终为特定输入提供相同的输出。相反，就SAMD而言，持续学习算法处于初级阶段，会根据传入的真实世界数据进行实时修改，而不需要控制软件版本发布。这种持续的学习有可能更好地处理当地人口的特征，但也有强化现有结构性偏见的风险。持续学习算法构成了最大的监管复杂性，似乎需要以特殊控制的形式进行持续监督，以确保持续的安全性和有效性。我们描述了持续学习算法的挑战，然后强调了正在开发的新证据标准和框架，并讨论了利益相关者参与的必要性。为了优化和实现SAMD的好处，监管机构需要处理两个关键步骤：第一，需要使用持续学习算法解决SAMD的独特性的国际标准和指导原则；第二，在整个产品生命周期中，并适合SAMD风险分类，监管机构、开发人员和SAMD最终用户之间需要持续沟通，以确保对该技术的警惕和准确理解。
Artificial intelligence (AI) is a broad discipline that aims to understand and design systems that display properties of intelligence. Machine learning (ML) is a subset of AI that describes how algorithms and models can assist computer systems in progressively improving their performance. In health care, an increasingly common application of AI/ML is software as a medical device (SaMD), which has the intention to diagnose, treat, cure, mitigate, or prevent disease. AI/ML includes either “locked” or “continuous learning” algorithms. Locked algorithms consistently provide the same output for a particular input. Conversely, continuous learning algorithms, in their infancy in terms of SaMD, modify in real-time based on incoming real-world data, without controlled software version releases. This continuous learning has the potential to better handle local population characteristics, but with the risk of reinforcing existing structural biases. Continuous learning algorithms pose the greatest regulatory complexity, requiring seemingly continuous oversight in the form of special controls to ensure ongoing safety and effectiveness. We describe the challenges of continuous learning algorithms, then highlight the new evidence standards and frameworks under development, and discuss the need for stakeholder engagement. The paper concludes with 2 key steps that regulators need to address in order to optimize and realize the benefits of SaMD: first, international standards and guiding principles addressing the uniqueness of SaMD with a continuous learning algorithm are required and second, throughout the product life cycle and appropriate to the SaMD risk classification, there needs to be continuous communication between regulators, developers, and SaMD end users to ensure vigilance and an accurate understanding of the technology.
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影响因子：
105.7
作者：
Vollmer, Sebastian;Mateen, Bilal A.;Hemingway, Harry
通讯作者：
Hemingway, Harry
影响因子：
7.4
作者：
Gilbert S;Fenech M;Hirsch M;Upadhyay S;Biasiucci A;Starlinger J
通讯作者：
Starlinger J
影响因子：
3.9
作者：
Unsworth H;Dillon B;Collinson L;Powell H;Salmon M;Oladapo T;Ayiku L;Shield G;Holden J;Patel N;Campbell M;Greaves F;Joshi I;Powell J;Tonnel A
通讯作者：
Tonnel A
影响因子：
82.9
作者：
Norgeot B;Quer G;Beaulieu-Jones BK;Torkamani A;Dias R;Gianfrancesco M;Arnaout R;Kohane IS;Saria S;Topol E;Obermeyer Z;Yu B;Butte AJ
通讯作者：
Butte AJ
影响因子：
82.9
作者：
Liu X;Cruz Rivera S;Moher D;Calvert MJ;Denniston AK;SPIRIT-AI and CONSORT-AI Working Group
通讯作者：
SPIRIT-AI and CONSORT-AI Working Group