Artificial intelligence (AI) and machine learning (ML) are rapidly evolving technologies with significant implications in obstetrics and midwifery. This systematic review aims to evaluate the latest advancements in AI and ML applications in obstetrics and midwifery. A search was conducted in three electronic databases (PubMed, Scopus, and Web of Science) for studies published between January 1, 2022, and February 20, 2025, using keywords related to AI, ML, obstetrics, and midwifery. The review adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for updated systematic reviews. Studies were selected based on their focus on AI/ML applications in obstetrics and midwifery, while non-English publications and review studies were excluded. The review included 64 studies, highlighting significant advancements in AI and ML applications across various domains in obstetrics and midwifery. Findings indicate that AI and ML models and systems achieved high accuracy in areas, such as assisted reproduction, diagnosis (e.g., 3D/4D ultrasound and MRI), pregnancy risk assessment (e.g., preeclampsia, gestational diabetes, preterm birth), fetal monitoring, mode of birth, and perinatal outcomes (e.g., mortality rates, postpartum hemorrhage, hypertensive disorders, neonatal respiratory distress). AI and ML have significant potential in transforming obstetric and midwifery care. The great number of studies reporting significant improvements suggests that the widespread adoption of AI and ML in these fields is imminent. Interdisciplinary collaboration between clinicians, data scientists, and policymakers will be crucial in shaping the future of maternal and neonatal healthcare.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11895402 | PMC |
http://dx.doi.org/10.7759/cureus.80394 | DOI Listing |
Cureus
March 2025
Department of Midwifery, Faculty of Health and Caring Sciences, University of West Attica, Athens, GRC.
Artificial intelligence (AI) and machine learning (ML) are rapidly evolving technologies with significant implications in obstetrics and midwifery. This systematic review aims to evaluate the latest advancements in AI and ML applications in obstetrics and midwifery. A search was conducted in three electronic databases (PubMed, Scopus, and Web of Science) for studies published between January 1, 2022, and February 20, 2025, using keywords related to AI, ML, obstetrics, and midwifery.
View Article and Find Full Text PDFGinekol Pol
March 2025
Chair and Clinical Department of Gynecology, Obstetrics and Gynecological Oncology, Medical University of Silesia, Katowice, Poland, Poland.
Objectives: The purpose of this study was to assess and compare the knowledge, attitudes, and practices of Polish midwives and obstetricians concerning external cephalic version (ECV), with particular attention to how professional qualifications, experience, and the reference level of the healthcare facility influenced these factors across both groups.
Material And Methods: An author-created, 22-question online survey was distributed separately to midwives and obstetricians, with each group receiving a questionnaire customized to assess their specific knowledge of ECV, professional experience, and attitudes toward the procedure.
Results: The study included 839 participants: 378 midwives and 461 physicians.
Midwifery
March 2025
Department of Public Health, Erasmus MC, University Medical Centre Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands; Department of Obstetrics and Gynaecology, Erasmus MC Sophia Children's Hospital, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands; Department of Neonatal and Pediatric Intensive Care, Division of Neonatology, Erasmus MC Sophia Children's Hospital, University Medical Centre Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
Objective: During the first COVID-19 lockdown in the Netherlands (9 March-1 June 2020), the homebirth rate increased from 27 % to 37 % among women with low-risk pregnancies starting labour in primary midwife-led care (overall population: 15 % in 2020). We explored characteristics and motivations of women who change their preference from a hospital birth to a home birth.
Design: A nationwide prospective online questionnaire.
J Eval Clin Pract
March 2025
Midwifery Department, Health Science Faculty, Marmara University, İstanbul, Turkey.
Background: Music as a distraction is used in various areas of obstetrics and gynecology to reduce fear, pain and anxiety.
Objective: In this study, it was aimed to determine the effect of music recital on labor anxiety and satisfaction.
Methods: The study was conducted in a hospital between June 12 and November 30, 2019.
J Perinat Neonatal Nurs
March 2025
Author Affiliations: College of Nursing, University of Colorado Anschutz Medical Campus, Aurora, Colorado (Drs Smith, Thumm, Barton, and Hernandez); Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado (Dr Giano); Biostatistics Core, University of Colorado Cancer Center, Aurora, Colorado (Ms Staley); Department of Obstetrics and Gynecology, University of Colorado Anschutz Medical Campus, School of Medicine, Colorado (Dr Sheeder); Division of Endocrinology, Metabolism, & Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado (Dr Hernandez); and Anschutz Medical Campus, Children's Hospital Colorado, Aurora, Colorado (Dr Hernandez).
Purpose: The study sought to identify differences in use of elective induction of labor (IOL) post-ARRIVE trial, by race and ethnicity, and contributions of multilevel contextual factors to induction use.
Background: Racial disparities in birth outcomes have been attributed to community and provider (ie, multilevel contextual) factors. The varied use of elective induction, a common obstetric procedure, can provide insights on how racial biases are evidenced in care delivery.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!