Yang Shiau-Ru, Chien Jen-Tzung, Lee Chen-Yi
Institute of Electrical and Computer EngineeringNational Yang Ming Chiao Tung University Hsinchu 30010 Taiwan.
IEEE Open J Eng Med Biol. 2024 Oct 23;6:147-151. doi: 10.1109/OJEMB.2024.3485534. eCollection 2025.
Owing to the rapid progress in artificial intelligence (AI) and the widespread use of generative learning, the problem of sparse data has been solved effectively in various research fields. The application of AI technologies has resulted in important transformations in healthcare, particularly in radiology. To ensure the high quality, safety, and effectiveness of AI and machine learning (ML) medical devices, the US Food and Drug Administration (FDA) has established regulatory guidelines to support the performance evaluation of medical devices. Furthermore, the FDA has proposed continuous surveillance requirements for AI/ML medical devices. This paper presents a summary of SaMD products that have passed the FDA 510 (k) AI/ML pathway, the challenges associated with the current AI/ML software-as-a-medical-device, and solutions for promoting the development of AI technologies in medicine. We hope to provide valuable information pertaining to medical-device design, development, and monitoring to ultimately achieve safer and more effective personalized medical services.
由于人工智能(AI)的迅速发展以及生成式学习的广泛应用,稀疏数据问题在各个研究领域都得到了有效解决。人工智能技术的应用给医疗保健领域带来了重大变革,尤其是在放射学方面。为确保人工智能和机器学习(ML)医疗设备的高质量、安全性和有效性,美国食品药品监督管理局(FDA)制定了监管指南以支持医疗设备的性能评估。此外,FDA还对人工智能/机器学习医疗设备提出了持续监测要求。本文概述了已通过FDA 510(k)人工智能/机器学习途径的软件即医疗器械(SaMD)产品、当前人工智能/机器学习软件作为医疗设备所面临的挑战,以及促进医学人工智能技术发展的解决方案。我们希望提供有关医疗设备设计、开发和监测的有价值信息,以最终实现更安全、更有效的个性化医疗服务。