Artificial Intelligence, Real-World Automation and the Safety of Medicines
Andrew Bate () and
Steve F. Hobbiger
Additional contact information
Andrew Bate: GSK
Steve F. Hobbiger: GSK
Drug Safety, 2021, vol. 44, issue 2, No 1, 125-132
Abstract:
Abstract Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, machine learning and automation across the pharmacovigilance lifecycle including pre-licensure. Reasons are provided on why adoption is challenging and we also provide a perspective on changes needed to accelerate adoption, and thereby improve patient safety. Last, we make clear that while technologies could be superimposed on existing pharmacovigilance processes for incremental improvements, these great societal advances in data and technology also provide us with a timely opportunity to reconsider everything we do in pharmacovigilance operations to maximise the benefit of these advances.
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s40264-020-01001-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:drugsa:v:44:y:2021:i:2:d:10.1007_s40264-020-01001-7
Ordering information: This journal article can be ordered from
http://www.springer.com/adis/journal/40264
DOI: 10.1007/s40264-020-01001-7
Access Statistics for this article
Drug Safety is currently edited by Nitin Joshi
More articles in Drug Safety from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().