ReviewCureus2025
Artificial Intelligence and Machine Learning: An Updated Systematic Review of Their Role in Obstetrics and Midwifery.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Racial Disparities and the Use of Artificial Intelligence for Predicting Maternal Mortality: A Literature Review.Epidemiologia (Basel, Switzerland) · 2026Review
- Nanostructured electrode materials and flexible-substrate engineering for wearable multi-analyte biosensors in diabetes monitoring and personalized care: a comprehensive review.Journal of materials science. Materials in medicine · 2026Review
- AI-powered population-based birth cohort study in the Western Province of Sri Lanka: study protocol.BMC pregnancy and childbirth · 2026Article
- The economic imperative of artificial intelligence in maternal and neonatal health: a review of evaluation benefits, frameworks, challenges, future perspectives, and limitations.Cost effectiveness and resource allocation : C/E · 2026Review
- AI-driven high-risk pregnancy prediction: balancing early detection, anxiety, and discrimination in digital public health.Frontiers in public health · 2026Article
- Development of Novel Predictive Scores for Obstetrical Risk Stratification in Adolescent Pregnancies: A Retrospective Study.Journal of clinical medicine · 2025Article
- Review
- Integrating Artificial Intelligence into Perinatal Care Pathways: A Scoping Review of Reviews of Applications, Outcomes, and Equity.Nursing reports (Pavia, Italy) · 2025Review
- Women's hormonal health research and care: the times, they are a-changin'.Translational cancer research · 2025Article
- Enhancing Obstetric Decision-Making With AI: A Systematic Review of AI Models for Predicting Mode of Delivery.Cureus · 2025Review
- AI-driven analysis of diabetes risk determinants in U.S. adults: Exploring disease prevalence and health factors.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.