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Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays
Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians’ decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, ra...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876883/ https://www.ncbi.nlm.nih.gov/pubmed/36697450 http://dx.doi.org/10.1038/s41598-023-28633-w |
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author | Gaube, Susanne Suresh, Harini Raue, Martina Lermer, Eva Koch, Timo K. Hudecek, Matthias F. C. Ackery, Alun D. Grover, Samir C. Coughlin, Joseph F. Frey, Dieter Kitamura, Felipe C. Ghassemi, Marzyeh Colak, Errol |
author_facet | Gaube, Susanne Suresh, Harini Raue, Martina Lermer, Eva Koch, Timo K. Hudecek, Matthias F. C. Ackery, Alun D. Grover, Samir C. Coughlin, Joseph F. Frey, Dieter Kitamura, Felipe C. Ghassemi, Marzyeh Colak, Errol |
author_sort | Gaube, Susanne |
collection | PubMed |
description | Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians’ decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice’s quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants’ confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare. |
format | Online Article Text |
id | pubmed-9876883 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98768832023-01-27 Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays Gaube, Susanne Suresh, Harini Raue, Martina Lermer, Eva Koch, Timo K. Hudecek, Matthias F. C. Ackery, Alun D. Grover, Samir C. Coughlin, Joseph F. Frey, Dieter Kitamura, Felipe C. Ghassemi, Marzyeh Colak, Errol Sci Rep Article Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians’ decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice’s quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants’ confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare. Nature Publishing Group UK 2023-01-25 /pmc/articles/PMC9876883/ /pubmed/36697450 http://dx.doi.org/10.1038/s41598-023-28633-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Gaube, Susanne Suresh, Harini Raue, Martina Lermer, Eva Koch, Timo K. Hudecek, Matthias F. C. Ackery, Alun D. Grover, Samir C. Coughlin, Joseph F. Frey, Dieter Kitamura, Felipe C. Ghassemi, Marzyeh Colak, Errol Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays |
title | Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays |
title_full | Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays |
title_fullStr | Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays |
title_full_unstemmed | Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays |
title_short | Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays |
title_sort | non-task expert physicians benefit from correct explainable ai advice when reviewing x-rays |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876883/ https://www.ncbi.nlm.nih.gov/pubmed/36697450 http://dx.doi.org/10.1038/s41598-023-28633-w |
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