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Artificial intelligence in systematic reviews: promising when appropriately used

BACKGROUND: Systematic reviews provide a structured overview of the available evidence in medical-scientific research. However, due to the increasing medical-scientific research output, it is a time-consuming task to conduct systematic reviews. To accelerate this process, artificial intelligence (AI...

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Autores principales: van Dijk, Sanne H B, Brusse-Keizer, Marjolein G J, Bucsán, Charlotte C, van der Palen, Job, Doggen, Carine J M, Lenferink, Anke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10335470/
https://www.ncbi.nlm.nih.gov/pubmed/37419641
http://dx.doi.org/10.1136/bmjopen-2023-072254
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author van Dijk, Sanne H B
Brusse-Keizer, Marjolein G J
Bucsán, Charlotte C
van der Palen, Job
Doggen, Carine J M
Lenferink, Anke
author_facet van Dijk, Sanne H B
Brusse-Keizer, Marjolein G J
Bucsán, Charlotte C
van der Palen, Job
Doggen, Carine J M
Lenferink, Anke
author_sort van Dijk, Sanne H B
collection PubMed
description BACKGROUND: Systematic reviews provide a structured overview of the available evidence in medical-scientific research. However, due to the increasing medical-scientific research output, it is a time-consuming task to conduct systematic reviews. To accelerate this process, artificial intelligence (AI) can be used in the review process. In this communication paper, we suggest how to conduct a transparent and reliable systematic review using the AI tool ‘ASReview’ in the title and abstract screening. METHODS: Use of the AI tool consisted of several steps. First, the tool required training of its algorithm with several prelabelled articles prior to screening. Next, using a researcher-in-the-loop algorithm, the AI tool proposed the article with the highest probability of being relevant. The reviewer then decided on relevancy of each article proposed. This process was continued until the stopping criterion was reached. All articles labelled relevant by the reviewer were screened on full text. RESULTS: Considerations to ensure methodological quality when using AI in systematic reviews included: the choice of whether to use AI, the need of both deduplication and checking for inter-reviewer agreement, how to choose a stopping criterion and the quality of reporting. Using the tool in our review resulted in much time saved: only 23% of the articles were assessed by the reviewer. CONCLUSION: The AI tool is a promising innovation for the current systematic reviewing practice, as long as it is appropriately used and methodological quality can be assured. PROSPERO REGISTRATION NUMBER: CRD42022283952.
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spelling pubmed-103354702023-07-12 Artificial intelligence in systematic reviews: promising when appropriately used van Dijk, Sanne H B Brusse-Keizer, Marjolein G J Bucsán, Charlotte C van der Palen, Job Doggen, Carine J M Lenferink, Anke BMJ Open Communication BACKGROUND: Systematic reviews provide a structured overview of the available evidence in medical-scientific research. However, due to the increasing medical-scientific research output, it is a time-consuming task to conduct systematic reviews. To accelerate this process, artificial intelligence (AI) can be used in the review process. In this communication paper, we suggest how to conduct a transparent and reliable systematic review using the AI tool ‘ASReview’ in the title and abstract screening. METHODS: Use of the AI tool consisted of several steps. First, the tool required training of its algorithm with several prelabelled articles prior to screening. Next, using a researcher-in-the-loop algorithm, the AI tool proposed the article with the highest probability of being relevant. The reviewer then decided on relevancy of each article proposed. This process was continued until the stopping criterion was reached. All articles labelled relevant by the reviewer were screened on full text. RESULTS: Considerations to ensure methodological quality when using AI in systematic reviews included: the choice of whether to use AI, the need of both deduplication and checking for inter-reviewer agreement, how to choose a stopping criterion and the quality of reporting. Using the tool in our review resulted in much time saved: only 23% of the articles were assessed by the reviewer. CONCLUSION: The AI tool is a promising innovation for the current systematic reviewing practice, as long as it is appropriately used and methodological quality can be assured. PROSPERO REGISTRATION NUMBER: CRD42022283952. BMJ Publishing Group 2023-07-07 /pmc/articles/PMC10335470/ /pubmed/37419641 http://dx.doi.org/10.1136/bmjopen-2023-072254 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Communication
van Dijk, Sanne H B
Brusse-Keizer, Marjolein G J
Bucsán, Charlotte C
van der Palen, Job
Doggen, Carine J M
Lenferink, Anke
Artificial intelligence in systematic reviews: promising when appropriately used
title Artificial intelligence in systematic reviews: promising when appropriately used
title_full Artificial intelligence in systematic reviews: promising when appropriately used
title_fullStr Artificial intelligence in systematic reviews: promising when appropriately used
title_full_unstemmed Artificial intelligence in systematic reviews: promising when appropriately used
title_short Artificial intelligence in systematic reviews: promising when appropriately used
title_sort artificial intelligence in systematic reviews: promising when appropriately used
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10335470/
https://www.ncbi.nlm.nih.gov/pubmed/37419641
http://dx.doi.org/10.1136/bmjopen-2023-072254
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