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

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.

Objective

The aim of this study is to explore the effect of user-interface design features on intensivists' trust in an ML-based clinical decision support system.

Methods

A total of 47 physicians from critical care specialties were presented with 3 patient cases of bacteremia in the setting of an ML-based simulation system. Three conditions of the simulation were tested according to co

SUBMITTER: Katzburg O 

PROVIDER: S-EPMC11310737 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature