IDEAS home Printed from https://ideas.repec.org/a/spr/drugsa/v45y2022i5d10.1007_s40264-022-01156-5.html
   My bibliography  Save this article

Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations

Author

Listed:
  • Jeffrey K. Aronson

    (Nuffield Department of Primary Care Health Sciences)

Abstract

The tools of artificial intelligence (AI) have enormous potential to enhance activities in pharmacovigilance. Pharmacovigilance experts need not be AI experts, but they should know enough about AI to explore the possibilities of collaboration with those who are. Modern concepts of AI date from Alan Turing’s work, especially his paper on “the imitation game”, in the late 1940s and early 1950s. Its scope today includes computational skills, including the formulation of mathematical proofs; visual perception, including facial recognition and virtual reality; decision making by expert systems; aspects of language, such as language processing, speech recognition, creative composition, and translation; and combinations of these, e.g. in self-driving vehicles. Machines can be programmed with the ability to learn, using neural networks that mimic cognitive actions of the human brain, leading to deep structural learning. Limitations of AI include difficulties with language, arising from the need to understand context and interpret ambiguities, which particularly affect translation, and inadequacies of databases, requiring careful preparation and curation. New techniques may cause unforeseen difficulties via unexpected malfunctioning. Relevant terms and concepts include different types of machine learning, neural networks, natural language programming, ontologies, and expert systems. Adoption of the tools of AI in pharmacovigilance has been slow. Machine learning, in conjunction with natural language processing and data mining, to study adverse drug reactions in databases such as those found in electronic health records, claims databases, and social media, has the potential to enhance the characterization of known adverse effects and reactions and detect new signals.

Suggested Citation

  • Jeffrey K. Aronson, 2022. "Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations," Drug Safety, Springer, vol. 45(5), pages 407-418, May.
    • Handle: RePEc:spr:drugsa:v:45:y:2022:i:5:d:10.1007_s40264-022-01156-5
    DOI: 10.1007/s40264-022-01156-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40264-022-01156-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40264-022-01156-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Acemoglu, Daron, 2021. "Harms of AI," CEPR Discussion Papers 16524, C.E.P.R. Discussion Papers.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Susan B. Shermock & Kenneth M. Shermock & Lotta L. Schepel, 2023. "Closed-Loop Medication Management with an Electronic Health Record System in U.S. and Finnish Hospitals," IJERPH, MDPI, vol. 20(17), pages 1-14, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kowalewski, Oskar & Pisany, Paweł, 2022. "Banks' consumer lending reaction to fintech and bigtech credit emergence in the context of soft versus hard credit information processing," International Review of Financial Analysis, Elsevier, vol. 81(C).
    2. Caselli, Mauro & Fracasso, Andrea, 2021. "Covid-19 and Technology," GLO Discussion Paper Series 1001, Global Labor Organization (GLO).
    3. Alonso, Cristian & Berg, Andrew & Kothari, Siddharth & Papageorgiou, Chris & Rehman, Sidra, 2022. "Will the AI revolution cause a great divergence?," Journal of Monetary Economics, Elsevier, vol. 127(C), pages 18-37.
    4. DELBONO Flavio & REGGIANI Carlo & SANDRINI Luca, 2021. "Strategic data sales to competing firms," JRC Working Papers on Digital Economy 2021-05, Joint Research Centre.
    5. Ilan Noy & Tomáš Uher, 2022. "Four New Horsemen of an Apocalypse? Solar Flares, Super-volcanoes, Pandemics, and Artificial Intelligence," Economics of Disasters and Climate Change, Springer, vol. 6(2), pages 393-416, July.
    6. Janine Berg & Francis Green & Laura Nurski & David A Spencer, 2023. "Risks to job quality from digital technologies: Are industrial relations in Europe ready for the challenge?," European Journal of Industrial Relations, , vol. 29(4), pages 347-365, December.
    7. Alessandro Sterlacchini, 2022. "AI Patenting and Employment: Evidence from the World's Top R&D Investors," Working Papers 462, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:drugsa:v:45:y:2022:i:5:d:10.1007_s40264-022-01156-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com/economics/journal/40264 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.