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

Details

• Alternative Title:
  Drug Discov Today
• Personal Author:
  Chen, Zhaoyi ; Liu, Xiong ; Hogan, William ; Shenkman, Elizabeth ; Bian, Jiang
• Description:
  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.
• Subjects:
  Article
• Source:
  Drug Discov Today. 26(5):1256-1264
• Pubmed ID:
  33358699
• Pubmed Central ID:
  PMC8626864
• Document Type:
  Journal Article
• Funding:
  U18 DP006512/DP/NCCDPHP CDC HHSUnited States/ ; R21 ES032762/ES/NIEHS NIH HHSUnited States/ ; R21 CA245858/CA/NCI NIH HHSUnited States/ ; UL1 TR001427/TR/NCATS NIH HHSUnited States/ ; R01 CA246418/CA/NCI NIH HHSUnited States/ ; U18DP006512/ACL/ACL HHSUnited States/ ; R21 AG068717/AG/NIA NIH HHSUnited States/
• Volume:
  26
• Issue:
  5
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:3e25042a69f4e9551a98367d85d7c3118671c26dd6b2f2c9dcda1bd391cf6a8e
• Download URL:
  https://stacks.cdc.gov/view/cdc/112046/cdc_112046_DS1.pdf
• File Type:
  [PDF - 613.48 KB ]