Abulibdeh Rawan, Celi Leo Anthony, Sejdić Ervin
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada.
Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLOS Digit Health. 2025 Jun 5;4(6):e0000866. doi: 10.1371/journal.pdig.0000866. eCollection 2025 Jun.
Artificial intelligence is rapidly transforming healthcare, offering promising advancements in diagnosis, treatment, and patient outcomes. However, concerns regarding the regulatory oversight of artificial intelligence driven medical technologies have emerged, particularly with the U.S. Food and Drug Administration's current approval processes. This paper critically examines the U.S. Food and Drug Administration's regulatory framework for artificial intelligence powered healthcare products, highlighting gaps in safety evaluations, post-market surveillance, and ethical considerations. Artificial intelligence's continuous learning capabilities introduce unique risks, as algorithms evolve beyond their initial validation, potentially leading to performance degradation and biased outcomes. Although the U.S. Food and Drug Administration has taken steps to address these challenges, such as artificial intelligence/machine learning-based software as a medical device action plan and proposed regulatory adjustments, significant weaknesses remain, particularly in real-time monitoring, transparency and bias mitigation. This paper argues for a more adaptive, community-engaged regulatory approach that mandates extensive post-market evaluations, requires artificial intelligence developers to disclose training data sources, and establishes enforceable standards for fairness, equity, and accountability. A patient-centered regulatory framework must also integrate diverse perspectives to ensure artificial intelligence technologies serve all populations equitably. By fostering an agile, transparent, and ethics-driven oversight system, the U.S. Food and Drug Administration can balance innovation with patient safety, ensuring that artificial intelligence-driven medical technologies enhance, rather than compromise, healthcare outcomes.
人工智能正在迅速改变医疗保健领域，在诊断、治疗和患者治疗效果方面带来了有前景的进展。然而，人们对人工智能驱动的医疗技术的监管监督产生了担忧，尤其是在美国食品药品监督管理局目前的审批流程方面。本文批判性地审视了美国食品药品监督管理局对人工智能驱动的医疗保健产品的监管框架，突出了安全评估、上市后监测和伦理考量方面的差距。人工智能的持续学习能力带来了独特的风险，因为算法在初始验证之后不断演变，可能导致性能下降和有偏差的结果。尽管美国食品药品监督管理局已采取措施应对这些挑战，如基于人工智能/机器学习的软件作为医疗器械的行动计划以及提议的监管调整，但重大弱点依然存在，尤其是在实时监测、透明度和偏差缓解方面。本文主张采取一种更具适应性、社区参与的监管方法，要求进行广泛的上市后评估，要求人工智能开发者披露训练数据来源，并建立公平、公正和问责的可执行标准。以患者为中心的监管框架还必须整合不同的观点，以确保人工智能技术公平地服务于所有人群。通过建立一个敏捷、透明和以伦理为驱动的监督系统，美国食品药品监督管理局可以在创新与患者安全之间取得平衡，确保人工智能驱动的医疗技术提升而非损害医疗保健效果。
