Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study

• Published in: PloS one Vol. 17; no. 12; p. e0278397
• Main Authors: Wichmann, Roberta Moreira, Fagundes, Thales Pardini, de Oliveira, Tiago Almeida, Batista, André Filipe de Moraes, Chiavegatto Filho, Alexandre Dias Porto
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.12.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Applications programs; Artificial Intelligence; Beliefs, opinions and attitudes; Computer and Information Sciences; Coronaviruses; COVID-19; COVID-19 - diagnosis; COVID-19 - epidemiology; Cross-Sectional Studies; Graphical representations; Health care; Health care industry; Humans; Innovations; Learning algorithms; Likert scale; Machine Learning; Mechanical ventilation; Medical personnel; Medical research; Medicine and Health Sciences; Mobile computing; Patients; People and Places; Physical Sciences; Physicians; Predictions; Professionals; Questionnaires; Research and Analysis Methods; Usability; Variables; Ventilation; Brazil
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0278397

Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms. A systematic search in MEDLINE, via PubMed, EMBASE and Web of Science was first performed. We developed a mobile app, RandomIA, to predict the occurrence of clinical outcomes, initially for COVID-19 and later expected to be expanded to other diseases. A questionnaire called System Usability Scale (SUS) was selected to assess the usability of the mobile app. A total of 69 doctors from the five regions of Brazil tested RandomIA and evaluated three different ways to visualize the predictions. For prognostic outcomes (mechanical ventilation, admission to an intensive care unit, and death), most doctors (62.9%) preferred a more complex visualization, represented by a bar graph with three categories (low, medium, and high probability) and a probability density graph for each outcome. For the diagnostic prediction of COVID-19, there was also a majority preference (65.4%) for the same option. Our results indicate that doctors could be more inclined to prefer receiving detailed results from predictive machine learning algorithms.