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Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond
An increasing number of studies on artificial intelligence (AI) are published in the dental and oral sciences. The reporting, but also further aspects of these studies, suffer from a range of limitations. Standards towards reporting, like the recently published Consolidated Standards of Reporting Tr...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8217901/ https://www.ncbi.nlm.nih.gov/pubmed/33655800 http://dx.doi.org/10.1177/0022034521998337 |
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author | Schwendicke, F. Krois, J. |
author_facet | Schwendicke, F. Krois, J. |
author_sort | Schwendicke, F. |
collection | PubMed |
description | An increasing number of studies on artificial intelligence (AI) are published in the dental and oral sciences. The reporting, but also further aspects of these studies, suffer from a range of limitations. Standards towards reporting, like the recently published Consolidated Standards of Reporting Trials (CONSORT)-AI extension can help to improve studies in this emerging field, and the Journal of Dental Research (JDR) encourages authors, reviewers, and readers to adhere to these standards. Notably, though, a wide range of aspects beyond reporting, located along various steps of the AI lifecycle, should be considered when conceiving, conducting, reporting, or evaluating studies on AI in dentistry. |
format | Online Article Text |
id | pubmed-8217901 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-82179012021-07-01 Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond Schwendicke, F. Krois, J. J Dent Res Departments An increasing number of studies on artificial intelligence (AI) are published in the dental and oral sciences. The reporting, but also further aspects of these studies, suffer from a range of limitations. Standards towards reporting, like the recently published Consolidated Standards of Reporting Trials (CONSORT)-AI extension can help to improve studies in this emerging field, and the Journal of Dental Research (JDR) encourages authors, reviewers, and readers to adhere to these standards. Notably, though, a wide range of aspects beyond reporting, located along various steps of the AI lifecycle, should be considered when conceiving, conducting, reporting, or evaluating studies on AI in dentistry. SAGE Publications 2021-03-03 2021-07 /pmc/articles/PMC8217901/ /pubmed/33655800 http://dx.doi.org/10.1177/0022034521998337 Text en © International & American Associations for Dental Research 2021 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Departments Schwendicke, F. Krois, J. Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond |
title | Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond |
title_full | Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond |
title_fullStr | Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond |
title_full_unstemmed | Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond |
title_short | Better Reporting of Studies on Artificial Intelligence: CONSORT-AI and Beyond |
title_sort | better reporting of studies on artificial intelligence: consort-ai and beyond |
topic | Departments |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8217901/ https://www.ncbi.nlm.nih.gov/pubmed/33655800 http://dx.doi.org/10.1177/0022034521998337 |
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