Applications of artificial intelligence in drug development using real-world data
This review addresses the need for insights into AI applications in drug development using RWD, but it is incremental as it synthesizes existing studies without introducing new methods or results.
The paper conducted a rapid review of studies from the past 20 years to provide an overview of how artificial intelligence (AI) is applied in drug development using real-world data (RWD), finding that the most popular applications include adverse event detection, trial recruitment, and drug repurposing.
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.