Clinics and Practice
Artificial Intelligence-Enabled Maternal and Fetal Monitoring Devices in Pregnancy: A Global Scoping Review and Evidence Mapping
By Victor Abiola Adepoju, Qorinah Estiningtyas Sakilah Adnani, Babatunde Ajayi Olofinbiyi and 4 others.
Abstract
Background
Artificial intelligence (AI) is increasingly used in obstetric ultrasound, cardiotocography, wearable fetal electrocardiography, and remote maternal-fetal monitoring. Evidence on real-world implementation, including feasibility, acceptability, workflow, task sharing, sustainability, equity, cost, and user experience, remains limited. This review aimed to map global real-world implementation evidence for AI-enabled obstetric monitoring and identify outcomes, determinants, gaps, and research priorities for scale-up.
Methods
We conducted a scoping review using the Arksey and O’Malley framework with Levac refinements, Joanna Briggs Institute guidance, and PRISMA-ScR. PubMed/MEDLINE, Web of Science, and Dimensions were searched from January 2010 to May 2026 and Scopus from January 2010 to December 2025. We included studies on AI-enabled obstetric monitoring in clinical or community settings and primary research on implementation readiness. We classified findings as operational (Tier 1) or readiness (Tier 2), mapped outcomes to RE-AIM, and interpreted determinants using NASSS.