Self-fulfilling prophecies and machine learning in resuscitation science

  • Maria De-Arteaga
    Information, Risk and Operations Management Department, McCombs School of Business, University of Texas at Austin, Austin, TX, USA
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  • Jonathan Elmer
    Corresponding author at: Iroquois Building, Suite 400A, 3600 Forbes Avenue, Pittsburgh, PA 15206, USA.
    Departments of Emergency Medicine, Critical Care Medicine and Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
    Search for articles by this author



      Growth of machine learning (ML) in healthcare has increased potential for observational data to guide clinical practice systematically. This can create self-fulfilling prophecies (SFPs), which arise when prediction of an outcome increases the chance that the outcome occurs.


      We performed a scoping review, searching PubMed and ArXiv using terms related to machine learning, algorithmic fairness and bias. We reviewed results and selected manuscripts for inclusion based on expert opinion of well-designed or key studies and review articles. We summarized these articles to explore how use of ML can create, perpetuate or compound SFPs, and offer recommendations to mitigate these risks.


      We identify-four key mechanisms through which SFPs may be reproduced or compounded by ML. First, imperfect human beliefs and behavior may be encoded as SFPs when treatment decisions are not accounted for. Since patient outcomes are influenced by a myriad of clinical actions, many of which are not collected in data, this is common. Second, human–machine interaction may compound SFPs through a cycle of mutual reinforcement. Third, ML may introduce new SFPs stemming from incorrect predictions. Finally, historically correct clinical choices may become SFPs in the face of medical progress.


      There is a need for broad recognition of SFPs as ML is increasingly applied in resuscitation science and across medicine. Acknowledging this challenge is crucial to inform research and practice that can transform ML from a tool that risks obfuscating and compounding SFPs into one that sheds light on and mitigates SFPs.


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