Department of Biomedical Sciences, Humanitas University, Milan, Italy.
IRCCS Istituto Clinico Humanitas, Milan, Italy.
JCO Clin Cancer Inform. 2023 Aug;7:e2300045. doi: 10.1200/CCI.23.00045.
Widespread interest in artificial intelligence (AI) in health care has focused mainly on deductive systems that analyze available real-world data to discover patterns not otherwise visible. Generative adversarial network, a new type of inductive AI, has recently evolved to generate high-fidelity virtual synthetic data (SD) trained on relatively limited real-world information. The AI system is fed with a collection of real data, and it learns to generate new augmented data while maintaining the general characteristics of the original data set. The use of SD to enhance clinical research and protect patient privacy has drawn a lot of interest in medicine and in the complex field of oncology. This article summarizes the main characteristics of this innovative technology and critically discusses how it can be used to accelerate data access for secondary purposes, providing an overview of the opportunities and challenges of SD generation for clinical cancer research and health care.
人工智能（AI）在医疗保健领域的广泛关注主要集中在演绎系统上，这些系统分析可用的现实世界数据以发现其他方式无法看到的模式。生成对抗网络，一种新型的归纳式 AI，最近已经发展到可以在相对有限的现实世界信息上生成高保真的虚拟合成数据（SD）。该 AI 系统接受一组真实数据的输入，并在学习生成新的增强数据的同时保持原始数据集的总体特征。使用 SD 来增强临床研究和保护患者隐私在医学和复杂的肿瘤学领域引起了极大的兴趣。本文总结了这项创新技术的主要特点，并批判性地讨论了它如何用于加速二次目的的数据访问，概述了 SD 生成在临床癌症研究和医疗保健中的机遇和挑战。
