Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses.

Journal: BMC medical research methodology
Published Date: Dec 20, 2021

Abstract

BACKGROUND: Systematic reviews are the cornerstone of evidence-based medicine. However, systematic reviews are time consuming and there is growing demand to produce evidence more quickly, while maintaining robust methods. In recent years, artificial intelligence and active-machine learning (AML) have been implemented into several SR software applications. As some of the barriers to adoption of new technologies are the challenges in set-up and how best to use these technologies, we have provided different situations and considerations for knowledge synthesis teams to consider when using artificial intelligence and AML for title and abstract screening.

Authors

  • Candyce Hamel
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada. cahamel@ohri.ca.
  • Mona Hersi
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
  • Shannon E Kelly
    Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
  • Andrea C Tricco
    Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, Ontario, M5B 1T8, Canada. Andrea.Tricco@unityhealth.to.
  • Sharon Straus
    Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada.
  • George Wells
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
  • Ba' Pham
    Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, Ontario, M5B 1T8, Canada.
  • Brian Hutton
    Knowledge Synthesis Unit, Ottawa Hospital Research Institute, Ottawa, ON, K1H 8L6, Canada.

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