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.
翻译:美国食品和药物管理局(FDA)一直积极促进在药物开发中使用真实世界数据(RWD),RWD可以产生重要的真实世界证据,反映使用治疗方法的真实世界临床环境,与此同时,人工智能(AI),特别是机器和深层学习(ML/DL)方法,在药物开发过程的许多阶段越来越多地被使用,AI的进步也为分析大型、多层面的RWD提供了新的战略。因此,我们对过去20年来的文章进行了快速审查,以概述使用AI和RWD的药物开发研究。我们发现,最流行的应用是负面事件检测、试用招聘和药物再利用。在这里,我们还讨论了目前的研究差距和未来的机会。