Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Dec 5, 2021
Date Accepted: Mar 15, 2022
Date Submitted to PubMed: Mar 17, 2022
(closed for review but you can still tweet)
Artificial Intelligence: a systematic review of 20 years of contributions reported in Obstetrics and Gynecology journals
ABSTRACT
Background:
The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Consequently, AI is applied across most Obstetrics and Gynecology domains (general obstetrics, gynecology surgery, fetal ultrasound, Assisted Reproductive Medicine…).
Objective:
The objective of this study was to provide a systematic review to establish the actual contributions of Artificial Intelligence (AI) reported in Obstetrics and Gynecology (OG/GYN) discipline journals.
Methods:
The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following MeSH terms: "obstetrics", “gynecology”, “reproductive techniques, assisted” or "pregnancy", between 01/01/2000 and 04/30/2020. All publications in Obstetrics and Gynecology core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no artificial intelligence process was used in the study. Review, editorial and commentary articles were also excluded. The study analysis comprises 1) the classification of publications into Obstetrics and Gynecology domains, 2) the description of AI methods, 3) the description of AI algorithms, 4) the description of datasets, 5) the description of AI contributions and 6) the description of the validation of the AI process.
Results:
The PubMed search retrieved 579 citations and 66 publications met the selection criteria. Both Machine Learning methods (n=39/66) and Knowledge Base methods (n=25/66) were represented. Machine Learning used imaging, numerical and clinical datasets. Knowledge Base methods used mostly omics datasets. Contributions of AI were method/algorithm development (53%), hypothesis generation (42%) or software development (3%). Validation was performed on 1 dataset (87%) and no external validation was reported.
Conclusions:
In Obstetrics and Gynecology discipline journals, preliminary work 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 reported in this review, hence updates need to be considered. Clinical Trial: N/A
Citation
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