Samala Ravi K, Gallas Brandon D, Zamzmi Ghada, Juluru Krishna, Khan Amir, Bahr Catherine, Ochs Robert, Carranza Dorn, Granstedt Jason, Margerrison Edward, Badano Aldo
Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Digital Health Center of Excellence, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
J Imaging Inform Med. 2025 Jan 29. doi: 10.1007/s10278-024-01374-6.
Continuous and consistent access to quality medical imaging data stimulates innovations in artificial intelligence (AI) technologies for patient care. Breakthrough innovations in data-driven AI technologies are founded on seamless communication between data providers, data managers, data users and regulators or other evaluators to determine the standards for quality data. However, the complexity in imaging data quality and heterogeneous nature of AI-enabled medical devices and their intended uses presents several challenges limiting the clinical translation of novel AI technologies. In this commentary, we discuss these challenges across different characteristics of data, such as data size, data labels, data diversity, data sequestration and reuse, and data drift. We discuss strategies around a data platform that incorporates protocols and checklists for ensuring data quality, tools and interactive guidelines that may help assess data diversity, study design and performance metrics for data usage and monitoring for data analytics. We envision this data platform to catalyze AI-enabled medical device innovation by providing a more efficient development and evaluation environment for bringing safe and effective AI technologies to the clinic.
持续且稳定地获取高质量医学影像数据，能够推动用于患者护理的人工智能（AI）技术创新。数据驱动型AI技术的突破性创新，建立在数据提供者、数据管理者、数据使用者以及监管者或其他评估者之间的无缝通信之上，以此来确定高质量数据的标准。然而，成像数据质量的复杂性以及人工智能医疗设备的异质性及其预期用途，带来了诸多挑战，限制了新型AI技术的临床转化。在这篇评论中，我们将针对数据的不同特征，如数据规模、数据标签、数据多样性、数据封存与再利用以及数据漂移等，探讨这些挑战。我们还将讨论围绕一个数据平台的策略，该平台纳入了用于确保数据质量的协议和清单、有助于评估数据多样性的工具和交互式指南、数据使用的研究设计和性能指标，以及用于数据分析的监测。我们设想这个数据平台能够通过提供一个更高效的开发和评估环境，将安全有效的AI技术引入临床，从而催化人工智能医疗设备的创新。
