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

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dc.contributor.authorWichmann, Roberta Moreira
dc.contributor.authorFagundes, Thales Pardini
dc.contributor.authorOliveira, Tiago Almeida de
dc.contributor.authorANDRE FILIPE DE MORAES BATISTA
dc.contributor.authorChiavegatto Filho, Alexandre Dias Porto
dc.creatorWichmann, Roberta Moreira
dc.creatorFagundes, Thales Pardini
dc.creatorOliveira, Tiago Almeida de
dc.creatorChiavegatto Filho, Alexandre Dias Porto
dc.date.accessioned2024-11-21T16:51:19Z
dc.date.available2024-11-21T16:51:19Z
dc.date.issued2022
dc.description.abstractArtificial 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.en
dc.formatDigital
dc.format.extent10 p.
dc.identifier.doi10.1371/journal.pone.0278397
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/7229
dc.language.isoInglês
dc.relation.ispartofPLoS One
dc.titlePhysician preference for receiving machine learning predictive results: A cross-sectional multicentric study
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278397
local.publisher.countryNão Informado
local.subject.cnpqCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
local.subject.cnpqCIENCIAS DA SAUDE::SAUDE COLETIVA::SAUDE PUBLICA
local.subject.cnpqCIENCIAS DA SAUDE::MEDICINA::CLINICA MEDICA
local.subject.cnpqCIENCIAS DA SAUDE::SAUDE COLETIVA::EPIDEMIOLOGIA
local.subject.cnpqENGENHARIAS::ENGENHARIA BIOMEDICA
local.subject.cnpqENGENHARIAS::ENGENHARIA BIOMEDICA::BIOENGENHARIA
local.subject.cnpqCIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::PROBABILIDADE E ESTATISTICA APLICADAS
local.typeArtigo Científico
publicationvolume.volumeNumber17
relation.isAuthorOfPublicationb10d272e-98b2-4953-8e51-37aea3fde20c
relation.isAuthorOfPublication.latestForDiscoveryb10d272e-98b2-4953-8e51-37aea3fde20c
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