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MAIC–10 brief quality checklist for publications using artificial intelligence and medical images
The use of artificial intelligence (AI) with medical images to solve clinical problems is becoming increasingly common, and the development of new AI solutions is leading to more studies and publications using this computational technology. As a novel research area, the use of common standards to ai...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9842808/ https://www.ncbi.nlm.nih.gov/pubmed/36645542 http://dx.doi.org/10.1186/s13244-022-01355-9 |
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author | Cerdá-Alberich, Leonor Solana, Jimena Mallol, Pedro Ribas, Gloria García-Junco, Miguel Alberich-Bayarri, Angel Marti-Bonmati, Luis |
author_facet | Cerdá-Alberich, Leonor Solana, Jimena Mallol, Pedro Ribas, Gloria García-Junco, Miguel Alberich-Bayarri, Angel Marti-Bonmati, Luis |
author_sort | Cerdá-Alberich, Leonor |
collection | PubMed |
description | The use of artificial intelligence (AI) with medical images to solve clinical problems is becoming increasingly common, and the development of new AI solutions is leading to more studies and publications using this computational technology. As a novel research area, the use of common standards to aid AI developers and reviewers as quality control criteria will improve the peer review process. Although some guidelines do exist, their heterogeneity and extension advocate that more explicit and simple schemes should be applied on the publication practice. Based on a review of existing AI guidelines, a proposal which collects, unifies, and simplifies the most relevant criteria was developed. The MAIC-10 (Must AI Criteria-10) checklist with 10 items was implemented as a guide to design studies and evaluate publications related to AI in the field of medical imaging. Articles published in Insights into Imaging in 2021 were selected to calculate their corresponding MAIC-10 quality score. The mean score was found to be 5.6 ± 1.6, with critical items present in most articles, such as “Clinical need”, “Data annotation”, “Robustness”, and “Transparency” present in more than 80% of papers, while improvements in other areas were identified. MAIC-10 was also observed to achieve the highest intra-observer reproducibility when compared to other existing checklists, with an overall reduction in terms of checklist length and complexity. In summary, MAIC-10 represents a short and simple quality assessment tool which is objective, robust and widely applicable to AI studies in medical imaging. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-022-01355-9. |
format | Online Article Text |
id | pubmed-9842808 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-98428082023-01-18 MAIC–10 brief quality checklist for publications using artificial intelligence and medical images Cerdá-Alberich, Leonor Solana, Jimena Mallol, Pedro Ribas, Gloria García-Junco, Miguel Alberich-Bayarri, Angel Marti-Bonmati, Luis Insights Imaging Critical Review The use of artificial intelligence (AI) with medical images to solve clinical problems is becoming increasingly common, and the development of new AI solutions is leading to more studies and publications using this computational technology. As a novel research area, the use of common standards to aid AI developers and reviewers as quality control criteria will improve the peer review process. Although some guidelines do exist, their heterogeneity and extension advocate that more explicit and simple schemes should be applied on the publication practice. Based on a review of existing AI guidelines, a proposal which collects, unifies, and simplifies the most relevant criteria was developed. The MAIC-10 (Must AI Criteria-10) checklist with 10 items was implemented as a guide to design studies and evaluate publications related to AI in the field of medical imaging. Articles published in Insights into Imaging in 2021 were selected to calculate their corresponding MAIC-10 quality score. The mean score was found to be 5.6 ± 1.6, with critical items present in most articles, such as “Clinical need”, “Data annotation”, “Robustness”, and “Transparency” present in more than 80% of papers, while improvements in other areas were identified. MAIC-10 was also observed to achieve the highest intra-observer reproducibility when compared to other existing checklists, with an overall reduction in terms of checklist length and complexity. In summary, MAIC-10 represents a short and simple quality assessment tool which is objective, robust and widely applicable to AI studies in medical imaging. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-022-01355-9. Springer Vienna 2023-01-16 /pmc/articles/PMC9842808/ /pubmed/36645542 http://dx.doi.org/10.1186/s13244-022-01355-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Critical Review Cerdá-Alberich, Leonor Solana, Jimena Mallol, Pedro Ribas, Gloria García-Junco, Miguel Alberich-Bayarri, Angel Marti-Bonmati, Luis MAIC–10 brief quality checklist for publications using artificial intelligence and medical images |
title | MAIC–10 brief quality checklist for publications using artificial intelligence and medical images |
title_full | MAIC–10 brief quality checklist for publications using artificial intelligence and medical images |
title_fullStr | MAIC–10 brief quality checklist for publications using artificial intelligence and medical images |
title_full_unstemmed | MAIC–10 brief quality checklist for publications using artificial intelligence and medical images |
title_short | MAIC–10 brief quality checklist for publications using artificial intelligence and medical images |
title_sort | maic–10 brief quality checklist for publications using artificial intelligence and medical images |
topic | Critical Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9842808/ https://www.ncbi.nlm.nih.gov/pubmed/36645542 http://dx.doi.org/10.1186/s13244-022-01355-9 |
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