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Artificial Intelligence, Real-World Automation and the Safety of Medicines

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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.

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  1. Clinical Safety and Pharmacovigilance, GSK, 980 Great West Road, Brentford, Middlesex, TW8 9GS, UK

    Andrew Bate & Steve F. Hobbiger

  2. Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK

    Andrew Bate

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Andrew Bate is a full-time employee of GSK, and holds stock and stock options at Pfizer and GSK. Steve F. Hobbiger is a full-time employee of GSK, and holds stock and stock options at GSK. The views expressed in this article represent the authors’ own thoughts and are independent of their employer.

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Bate, A., Hobbiger, S.F. Artificial Intelligence, Real-World Automation and the Safety of Medicines. Drug Saf 44, 125–132 (2021). https://doi.org/10.1007/s40264-020-01001-7

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