Santra Snigdha, Kukreja Preet, Saxena Kinshuk, Gandhi Sanyam, Singh Om V
Chugai Pharma, Berkeley, NJ, United States.
Department of Population Health, Episcopal Health Services, New York, NY, United States.
Front Med Technol. 2024 Nov 28;6:1473350. doi: 10.3389/fmedt.2024.1473350. eCollection 2024.
In recent years, the Artificial Intelligence (AI) has enabled conventional Combination Devices (CDs) to innovate in healthcare merging with technology sectors. However, the challenges like reliance on predicate devices in US Food and Drug Administration (FDA's 510(k) pathway, especially for perpetually updating AI are stressed. Though the European Union (EU's new Medical Device Regulations address software and AI, fitting adaptive algorithms into conformity assessments remains difficult. The urgent need for frameworks cognizant of AI risks like model degradation and data biases is emphasized. Insights from recalled devices and case studies elucidate challenges in regulatory navigation for manufacturers. Adaptive policy frameworks balancing patient safeguards and rapid innovation are proposed. Recommendations target regulators and policy makers, advocating global standards to enable safe, effective and equitable AI adoption. This article aims to examine AI-enabled combination device regulation, inspecting US and EU strategies as well as obstacles for manufacturers and regulators.
近年来,人工智能(AI)使传统组合设备(CDs)能够与技术领域融合,在医疗保健方面进行创新。然而,人们强调了一些挑战,比如在美国食品药品监督管理局(FDA)的510(k)途径中依赖谓词设备,尤其是对于不断更新的人工智能。尽管欧盟(EU)的新医疗器械法规涉及软件和人工智能,但将自适应算法纳入合格评定仍然困难。强调迫切需要认识到人工智能风险(如模型退化和数据偏差)的框架。召回设备的见解和案例研究阐明了制造商在监管导航方面的挑战。提出了平衡患者保护和快速创新的自适应政策框架。建议针对监管机构和政策制定者,倡导全球标准以实现安全、有效和公平地采用人工智能。本文旨在研究人工智能驱动的组合设备监管,审视美国和欧盟的策略以及制造商和监管机构面临的障碍。