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Title: Applications of artificial intelligence in drug development using real-world data.

Authors: Chen, Zhaoyi; Liu, Xiong; Hogan, William; Shenkman, Elizabeth; Bian, Jiang

Published In Drug Discov Today, (2021 05)

Abstract: 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.

PubMed ID: 33358699 Exiting the NIEHS site

MeSH Terms: Animals; Artificial Intelligence*; Deep Learning; Drug Development/methods*; Drug Repositioning; Humans; Machine Learning*; United States; United States Food and Drug Administration

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