Integrating Artificial Intelligence into Perinatal Care Pathways: A Scoping Review of Reviews of Applications, Outcomes, and Equity.
Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been...
| Publicado en: | Nursing Reports Vol. 15; no. 8; pp. 281 - 305 |
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| Autores principales: | , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
MDPI
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187615779&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187615779 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2039439X EGNT jtl: Nursing Reports issn: 2039439X maglogo: N pubinfo: dt: Aug2025 vid: 15 iid: 8 pid: 97109 pub: MDPI artinfo: ui: 187615779 187615779 187615779 10.3390/nursrep15080281 187615779 ppf: 281 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Integrating Artificial Intelligence into Perinatal Care Pathways: A Scoping Review of Reviews of Applications, Outcomes, and Equity. aug: au: El Arab, Rabie Adel Al Moosa, Omayma Abdulaziz Albahrani, Zahraa Alkhalil, Israa Somerville, Joel Abuadas, Fuad affil: Almoosa College of Health Sciences, Alhsa 36422, Saudi Arabia sug: subj: Artificial Intelligence Utilization Machine Learning Utilization Digital Health Perinatal Care Reproductive Health Sexual Health Outcomes (Health Care) Health Services Accessibility Funding Source Human Scoping Review PubMed Embase Cochrane Library CINAHL Database Medline Checklists Equality Thematic Analysis Prepregnancy Care Pregnancy Female Fertility Convolutional Neural Networks Follicle-Stimulating Hormone Chatbot Patient Education Pre-Exposure Prophylaxis Patient Compliance Safe Sex Privacy and Confidentiality Deep Learning Ultrasonography, Prenatal Wearable Sensors Remote Patient Monitoring Heart Rate Infant Mortality Prevention and Control Infant, Very Low Birth Weight Neonatal Sepsis Retinopathy of Prematurity Enterocolitis, Necrotizing ROC Curve Resource-Limited Settings Africa South of the Sahara Maternal-Child Health Maternal-Child Care Risk Assessment Reproduction Techniques Cost Effectiveness Analysis Embryo Prediction Models Gonadotropins Administration and Dosage Mental Health Health Screening Postpartum Hemorrhage Risk Factors Telehealth Intensive Care Units, Neonatal Decision Support Techniques Infant Development Workflow Semen Analysis Health Resource Allocation Artificial Intelligence Ethical Issues Female ab: Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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