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Unintended consequences of machine learning in medicine?
Machine learning (ML) has the potential to significantly aid medical practice. However, a recent article highlighted some negative consequences that may arise from using ML decision support in medicine. We argue here that whilst the concerns raised by the authors may be appropriate, they are not spe...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701440/ https://www.ncbi.nlm.nih.gov/pubmed/29250316 http://dx.doi.org/10.12688/f1000research.12693.1 |
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author | McDonald, Laura Ramagopalan, Sreeram V. Cox, Andrew P. Oguz, Mustafa |
author_facet | McDonald, Laura Ramagopalan, Sreeram V. Cox, Andrew P. Oguz, Mustafa |
author_sort | McDonald, Laura |
collection | PubMed |
description | Machine learning (ML) has the potential to significantly aid medical practice. However, a recent article highlighted some negative consequences that may arise from using ML decision support in medicine. We argue here that whilst the concerns raised by the authors may be appropriate, they are not specific to ML, and thus the article may lead to an adverse perception about this technique in particular. Whilst ML is not without its limitations like any methodology, a balanced view is needed in order to not hamper its use in potentially enabling better patient care. |
format | Online Article Text |
id | pubmed-5701440 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-57014402017-12-14 Unintended consequences of machine learning in medicine? McDonald, Laura Ramagopalan, Sreeram V. Cox, Andrew P. Oguz, Mustafa F1000Res Correspondence Machine learning (ML) has the potential to significantly aid medical practice. However, a recent article highlighted some negative consequences that may arise from using ML decision support in medicine. We argue here that whilst the concerns raised by the authors may be appropriate, they are not specific to ML, and thus the article may lead to an adverse perception about this technique in particular. Whilst ML is not without its limitations like any methodology, a balanced view is needed in order to not hamper its use in potentially enabling better patient care. F1000 Research Limited 2017-09-19 /pmc/articles/PMC5701440/ /pubmed/29250316 http://dx.doi.org/10.12688/f1000research.12693.1 Text en Copyright: © 2017 McDonald L et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Correspondence McDonald, Laura Ramagopalan, Sreeram V. Cox, Andrew P. Oguz, Mustafa Unintended consequences of machine learning in medicine? |
title | Unintended consequences of machine learning in medicine? |
title_full | Unintended consequences of machine learning in medicine? |
title_fullStr | Unintended consequences of machine learning in medicine? |
title_full_unstemmed | Unintended consequences of machine learning in medicine? |
title_short | Unintended consequences of machine learning in medicine? |
title_sort | unintended consequences of machine learning in medicine? |
topic | Correspondence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701440/ https://www.ncbi.nlm.nih.gov/pubmed/29250316 http://dx.doi.org/10.12688/f1000research.12693.1 |
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