In a pilot study, automated real-time systematic review updates were feasible, accurate, and work-saving.

Journal: Journal of clinical epidemiology
Published Date: Jan 1, 2023

Abstract

OBJECTIVES: The aim of this study is to describe and pilot a novel method for continuously identifying newly published trials relevant to a systematic review, enabled by combining artificial intelligence (AI) with human expertise.

Authors

  • Iain J Marshall
    Department of Primary Care and Public Health Sciences, King's College London, UK iain.marshall@kcl.ac.uk.
  • Thomas A Trikalinos
    Center for Evidence Synthesis in Health, Brown University, Providence, USA.
  • Frank Soboczenski
    School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 3rd Floor, Addison House, Guy's Campus, London, SE1 1UL, UK. frank.soboczenski@kcl.ac.uk.
  • Hye Sun Yun
    Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States.
  • Gregory Kell
    School of Life Course and Population Sciences, King's College London, London, UK.
  • Rachel Marshall
    Independent Researcher, London, UK.
  • Byron C Wallace
    School of Information, University of Texas at Austin, Austin, Texas, USA.

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