The Impact of Information Relevancy and Interactivity on Intensivists' Trust in a Machine Learning-Based Bacteremia Prediction System: Simulation Study.

Journal: JMIR human factors
PMID: 39092520

Abstract

BACKGROUND: The exponential growth in computing power and the increasing digitization of information have substantially advanced the machine learning (ML) research field. However, ML algorithms are often considered "black boxes," and this fosters distrust. In medical domains, in which mistakes can result in fatal outcomes, practitioners may be especially reluctant to trust ML algorithms.

Authors

  • Omer Katzburg
    Department of Health Policy and Management, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
  • Michael Roimi
    Department of Critical Care Medicine, Rambam Health Care Campus, Haifa, Israel.
  • Amit Frenkel
    General Intensive Care Unit, Soroka Medical Center, Be'er Sheva, Israel.
  • Roy Ilan
    General Intensive Care Unit, Rambam Medical Center, Haifa, Israel.
  • Yuval Bitan
    Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, POB 653, Beer Sheva, Israel. yuval@bitan.net.

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