  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.
