2026-10-11T09:33:03Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33954958 2026-09-21T15:30:23Z category_131 portal_316 item_type_3 month_year_09_2026

Supplementary file 1_Artificial intelligence and obstetric anesthesia: a scoping review.docx Maeve F. Givens (25084147) Edward Rodriguez Caceres (25084150) Rohan Jotwani (9633462) John E. Rubin (25084153) Peter A. Goldstein (25084156) Robert S. White (7468469) Anesthesiology artificial intelligence machine learning obstetric anesthesia predictive model scoping review Background<p>Artificial intelligence (AI) is expanding rapidly across anesthesiology, with growing applications in obstetric anesthesia at a time when maternal mortality in the United States remains high and marked racial and ethnic disparities persist. This scoping review aimed to characterize the current uses, emerging applications, and future directions of AI across the field of obstetric anesthesiology.</p>Methods<p>We searched PubMed and Embase, combining clinical terms for obstetric anesthesia and delivery with methodological terms for AI and machine learning (ML). English language original studies, systematic reviews, clinical trials, and meta-analyses published between January 2015 and December 2025 describing the development, validation, or clinical application of AI/ML tools relevant to obstetric anesthesia were eligible. Records were screened by two reviewers with consensus adjudication by two board-certified anesthesiologists. Included studies were categorized by AI domain, ML class, intended application, validation approach, and aspect of care.</p>Results<p>Of 3,135 records identified, 172 met inclusion criteria for review, comprising 154 original research studies, 17 systematic or narrative reviews, and 1 case report. 12 other records encompassing 9 registered clinical trials or protocols and 3 workforce-focused studies were also identified and reported upon separately. 69% of publications appeared after 2022. The most common domains were maternal risk and complications prediction (n = 61), delivery mode prediction (n = 43), and patient education and conversational AI (n = 15). Reported model discrimination was frequently strong, with areas under the curve (AUC) commonly between 0.75 and 0.95. However, only 14% of the 154 original research studies reported external validation. Prospective evaluations were rare, and almost none described integration into clinical workflow.</p>Conclusions<p>The use of AI in obstetric anesthesia has accelerated rapidly, spanning maternal risk prediction, delivery planning, fetal monitoring, patient education, and provider training. Although discrimination metrics are generally strong, the literature remains largely retrospective, with limited external validation and little evidence of real-world clinical impact. Future efforts must prioritize externally validated, prospectively evaluated, and workflow-integrated studies if AI is to meaningfully reduce preventable maternal morbidity and mortality.</p> 2026-09-21T15:30:23Z Dataset Dataset 10.3389/fanes.2026.1896595.s001 https://figshare.com/articles/dataset/Supplementary_file_1_Artificial_intelligence_and_obstetric_anesthesia_a_scoping_review_docx/33954958 CC BY 4.0