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Best practices for authors of healthcare-related artificial intelligence manuscripts
Since its inception in 2017, npj Digital Medicine has attracted a disproportionate number of manuscripts reporting on uses of artificial intelligence. This field has matured rapidly in the past several years. There was initial fascination with the algorithms themselves (machine learning, deep learni...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567805/ https://www.ncbi.nlm.nih.gov/pubmed/33083569 http://dx.doi.org/10.1038/s41746-020-00336-w |
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author | Kakarmath, Sujay Esteva, Andre Arnaout, Rima Harvey, Hugh Kumar, Santosh Muse, Evan Dong, Feng Wedlund, Leia Kvedar, Joseph |
author_facet | Kakarmath, Sujay Esteva, Andre Arnaout, Rima Harvey, Hugh Kumar, Santosh Muse, Evan Dong, Feng Wedlund, Leia Kvedar, Joseph |
author_sort | Kakarmath, Sujay |
collection | PubMed |
description | Since its inception in 2017, npj Digital Medicine has attracted a disproportionate number of manuscripts reporting on uses of artificial intelligence. This field has matured rapidly in the past several years. There was initial fascination with the algorithms themselves (machine learning, deep learning, convoluted neural networks) and the use of these algorithms to make predictions that often surpassed prevailing benchmarks. As the discipline has matured, individuals have called attention to aberrancies in the output of these algorithms. In particular, criticisms have been widely circulated that algorithmically developed models may have limited generalizability due to overfitting to the training data and may systematically perpetuate various forms of biases inherent in the training data, including race, gender, age, and health state or fitness level (Challen et al. BMJ Qual. Saf. 28:231–237, 2019; O’neil. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Broadway Book, 2016). Given our interest in publishing the highest quality papers and the growing volume of submissions using AI algorithms, we offer a list of criteria that authors should consider before submitting papers to npj Digital Medicine. |
format | Online Article Text |
id | pubmed-7567805 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75678052020-10-19 Best practices for authors of healthcare-related artificial intelligence manuscripts Kakarmath, Sujay Esteva, Andre Arnaout, Rima Harvey, Hugh Kumar, Santosh Muse, Evan Dong, Feng Wedlund, Leia Kvedar, Joseph NPJ Digit Med Editorial Since its inception in 2017, npj Digital Medicine has attracted a disproportionate number of manuscripts reporting on uses of artificial intelligence. This field has matured rapidly in the past several years. There was initial fascination with the algorithms themselves (machine learning, deep learning, convoluted neural networks) and the use of these algorithms to make predictions that often surpassed prevailing benchmarks. As the discipline has matured, individuals have called attention to aberrancies in the output of these algorithms. In particular, criticisms have been widely circulated that algorithmically developed models may have limited generalizability due to overfitting to the training data and may systematically perpetuate various forms of biases inherent in the training data, including race, gender, age, and health state or fitness level (Challen et al. BMJ Qual. Saf. 28:231–237, 2019; O’neil. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Broadway Book, 2016). Given our interest in publishing the highest quality papers and the growing volume of submissions using AI algorithms, we offer a list of criteria that authors should consider before submitting papers to npj Digital Medicine. Nature Publishing Group UK 2020-10-16 /pmc/articles/PMC7567805/ /pubmed/33083569 http://dx.doi.org/10.1038/s41746-020-00336-w Text en © The Author(s) 2020 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Editorial Kakarmath, Sujay Esteva, Andre Arnaout, Rima Harvey, Hugh Kumar, Santosh Muse, Evan Dong, Feng Wedlund, Leia Kvedar, Joseph Best practices for authors of healthcare-related artificial intelligence manuscripts |
title | Best practices for authors of healthcare-related artificial intelligence manuscripts |
title_full | Best practices for authors of healthcare-related artificial intelligence manuscripts |
title_fullStr | Best practices for authors of healthcare-related artificial intelligence manuscripts |
title_full_unstemmed | Best practices for authors of healthcare-related artificial intelligence manuscripts |
title_short | Best practices for authors of healthcare-related artificial intelligence manuscripts |
title_sort | best practices for authors of healthcare-related artificial intelligence manuscripts |
topic | Editorial |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567805/ https://www.ncbi.nlm.nih.gov/pubmed/33083569 http://dx.doi.org/10.1038/s41746-020-00336-w |
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