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Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review

BACKGROUND: The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artific...

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Autores principales: Dhombres, Ferdinand, Bonnard, Jules, Bailly, Kévin, Maurice, Paul, Papageorghiou, Aris T, Jouannic, Jean-Marie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069308/
https://www.ncbi.nlm.nih.gov/pubmed/35297766
http://dx.doi.org/10.2196/35465
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author Dhombres, Ferdinand
Bonnard, Jules
Bailly, Kévin
Maurice, Paul
Papageorghiou, Aris T
Jouannic, Jean-Marie
author_facet Dhombres, Ferdinand
Bonnard, Jules
Bailly, Kévin
Maurice, Paul
Papageorghiou, Aris T
Jouannic, Jean-Marie
author_sort Dhombres, Ferdinand
collection PubMed
description BACKGROUND: The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others. OBJECTIVE: The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals. METHODS: The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process. RESULTS: The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals. CONCLUSIONS: In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.
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spelling pubmed-90693082022-05-05 Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review Dhombres, Ferdinand Bonnard, Jules Bailly, Kévin Maurice, Paul Papageorghiou, Aris T Jouannic, Jean-Marie J Med Internet Res Review BACKGROUND: The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others. OBJECTIVE: The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals. METHODS: The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process. RESULTS: The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals. CONCLUSIONS: In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered. JMIR Publications 2022-04-20 /pmc/articles/PMC9069308/ /pubmed/35297766 http://dx.doi.org/10.2196/35465 Text en ©Ferdinand Dhombres, Jules Bonnard, Kévin Bailly, Paul Maurice, Aris T Papageorghiou, Jean-Marie Jouannic. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.04.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Review
Dhombres, Ferdinand
Bonnard, Jules
Bailly, Kévin
Maurice, Paul
Papageorghiou, Aris T
Jouannic, Jean-Marie
Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review
title Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review
title_full Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review
title_fullStr Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review
title_full_unstemmed Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review
title_short Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review
title_sort contributions of artificial intelligence reported in obstetrics and gynecology journals: systematic review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069308/
https://www.ncbi.nlm.nih.gov/pubmed/35297766
http://dx.doi.org/10.2196/35465
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