Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study.
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 doctor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0278397 |
| _version_ | 1797954068592721920 |
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| author | Roberta Moreira Wichmann Thales Pardini Fagundes Tiago Almeida de Oliveira André Filipe de Moraes Batista Alexandre Dias Porto Chiavegatto Filho |
| author_facet | Roberta Moreira Wichmann Thales Pardini Fagundes Tiago Almeida de Oliveira André Filipe de Moraes Batista Alexandre Dias Porto Chiavegatto Filho |
| author_sort | Roberta Moreira Wichmann |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T23:11:42Z |
| format | Article |
| id | doaj.art-f0522715ed0147a59c89773fe2d95c5b |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T23:11:42Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f0522715ed0147a59c89773fe2d95c5b2023-01-13T05:31:33ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011712e027839710.1371/journal.pone.0278397Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study.Roberta Moreira WichmannThales Pardini FagundesTiago Almeida de OliveiraAndré Filipe de Moraes BatistaAlexandre Dias Porto Chiavegatto FilhoArtificial 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.https://doi.org/10.1371/journal.pone.0278397 |
| spellingShingle | Roberta Moreira Wichmann Thales Pardini Fagundes Tiago Almeida de Oliveira André Filipe de Moraes Batista Alexandre Dias Porto Chiavegatto Filho Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study. PLoS ONE |
| title | Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study. |
| title_full | Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study. |
| title_fullStr | Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study. |
| title_full_unstemmed | Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study. |
| title_short | Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study. |
| title_sort | physician preference for receiving machine learning predictive results a cross sectional multicentric study |
| url | https://doi.org/10.1371/journal.pone.0278397 |
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