Gallos Parisis, Matragkas Nicholas, Islam Saif Ul, Epiphaniou Gregory, Hansen Scott, Harrison Stuart, van Dijk Bram, Haas Marcel, Pappous Giorgos, Brouwer Simon, Torlontano Francesco, Abbasi Saadullah Farooq, Pournik Omid, Churm James, Mantas John, Parra-Calderón Carlos Luis, Petkousis Dimitrios, Weber Patrick, Dzingina Benjamin, Mraidha Chokri, Maple Carsten, Achterberg Jim, Spruit Marco, Saratsioti Evi, Moustaghfir Younes, Arvanitis Theodoros N
European Federation for Medical Informatics, Switzerland.
CEA, List, Université Paris-Saclay, France.
Stud Health Technol Inform. 2024 Aug 22;316:1193-1197. doi: 10.3233/SHTI240624.
Digital health solutions hold promise for enhancing healthcare delivery and patient outcomes, primarily driven by advancements such as machine learning, artificial intelligence, and data science, which enable the development of integrated care systems. Techniques for generating synthetic data from real datasets are highly advanced and continually evolving. This paper aims to present the INSAFEDARE project's ambition regarding medical devices' regulation and how real and synthetic data can be used to check if devices are safe and effective. The project will consist of three pillars: a) assurance of new state-of-the-art technologies and approaches (such as synthetic data), which will support the validation methods as part of regulatory decision-making; b) technical and scientific, focusing on data-based safety assurance, as well as discovery, integration and use of datasets, and use of machine learning approaches; and c) delivery to practice, through co-production involving relevant stakeholders, dissemination and sustainability of the project's outputs. Finally, INSAFEDARE will develop an open syllabus and training certification for health professionals focused on quality assurance.
数字健康解决方案有望改善医疗服务提供和患者治疗效果，这主要得益于机器学习、人工智能和数据科学等技术进步，这些进步推动了综合护理系统的发展。从真实数据集生成合成数据的技术非常先进且不断发展。本文旨在介绍INSAFEDARE项目在医疗器械监管方面的目标，以及如何利用真实数据和合成数据来检验器械是否安全有效。该项目将包括三个支柱：a）确保采用新的先进技术和方法（如合成数据），这将作为监管决策的一部分支持验证方法；b）技术和科学支柱，侧重于基于数据的安全保证，以及数据集的发现、整合和使用，以及机器学习方法的应用；c）通过相关利益攸关方的共同参与、项目成果的传播和可持续性，将成果应用于实践。最后，INSAFEDARE将为专注于质量保证的卫生专业人员制定开放课程和培训认证。
