Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610-0177, USA.
AI Innovation Center, Novartis, Cambridge, MA 02142, USA.
Drug Discov Today. 2021 May;26(5):1256-1264. doi: 10.1016/j.drudis.2020.12.013. Epub 2020 Dec 24.
The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. Meanwhile, artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, have been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. Thus, we conducted a rapid review of articles from the past 20 years, to provide an overview of the drug development studies that use both AI and RWD. We found that the most popular applications were adverse event detection, trial recruitment, and drug repurposing. Here, we also discuss current research gaps and future opportunities.
美国食品和药物管理局（FDA）一直在积极推动在药物开发中使用真实世界数据（RWD）。RWD 可以生成反映治疗实际应用的真实世界临床环境的重要真实世界证据。同时，人工智能（AI），特别是机器和深度学习（ML/DL）方法，已在药物开发过程的许多阶段得到越来越多的应用。人工智能的进步也为分析大型多维 RWD 提供了新策略。因此，我们对过去 20 年的文章进行了快速回顾，以提供使用 AI 和 RWD 的药物开发研究概述。我们发现最受欢迎的应用是不良事件检测、试验招募和药物再利用。在这里，我们还讨论了当前的研究差距和未来的机会。
