When Medical Devices Have a Mind of Their Own: The Challenges of Regulating Artificial Intelligence

IF 0.5 4区 社会学 Q3 LAW
Jessa Boubker
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引用次数: 2

Abstract

How can an agency like the U.S. Food & Drug Administration (“FDA”) effectively regulate software that is constantly learning and adapting to real-world data? Continuously learning algorithms pose significant public health risks if a medical device can change overtime to fundamentally alter the nature of a device post-market. This Article evaluates the FDA’s proposed regulatory framework for artificially intelligent medical devices against the backdrop of the current technology, as well as industry professionals’ desired trajectory, to determine whether the proposed regulatory framework can ensure safe and reliable medical devices without stifling innovation. Ultimately, the FDA succeeds in placing effective limits on continuously learning algorithms while giving manufacturers freedom to allow their devices to adapt to real-world data. The framework, however, does not give adequate attention to protecting patient data, monitoring cybersecurity, and ensuring safety and efficacy. The FDA, medical device industry, and relevant policymakers should increase oversight of these areas to protect patients and providers relying on this new technology.
当医疗设备有了自己的思想:监管人工智能的挑战
像美国食品和药物管理局(FDA)这样的机构如何有效地监管不断学习和适应现实世界数据的软件?如果医疗设备可以随着时间的推移而从根本上改变设备上市后的性质,那么持续学习算法将构成重大的公共卫生风险。本文将在当前技术背景下评估FDA提出的人工智能医疗器械监管框架,以及行业专业人士的期望轨迹,以确定拟议的监管框架是否能够在不扼杀创新的情况下确保医疗器械的安全可靠。最终,FDA成功地对持续学习算法进行了有效限制,同时给予制造商允许其设备适应现实世界数据的自由。然而,该框架并未对保护患者数据、监控网络安全以及确保安全性和有效性给予足够的重视。FDA、医疗器械行业和相关政策制定者应该加强对这些领域的监督,以保护依赖这项新技术的患者和提供者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.80
自引率
16.70%
发文量
8
期刊介绍: desde Enero 2004 Último Numero: Octubre 2008 AJLM will solicit blind comments from expert peer reviewers, including faculty members of our editorial board, as well as from other preeminent health law and public policy academics and professionals from across the country and around the world.
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