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.

像美国食品和药物管理局 (“FDA”) 这样的机构如何有效监管不断学习和适应现实世界数据的软件？如果医疗设备可以加班更改以从根本上改变设备上市后的性质，则持续学习算法会带来重大的公共卫生风险。本文在当前技术背景下评估 FDA 提议的人工智能医疗器械监管框架，以及行业专业人士的预期轨迹，以确定提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安全可靠。最终，FDA 成功地对持续学习算法设置了有效限制，同时让制造商自由地允许他们的设备适应现实世界的数据。然而，该框架并未充分关注保护患者数据、监控网络安全以及确保安全性和有效性。FDA、医疗器械行业和相关政策制定者应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。