A Prospective Review on Application of Artificial Intelligence in Maternal Health Nursing

Abstract

Background: Maternal morbidity and mortality remain a primary global public health concern. Artificial intelligence (AI) has emerged as a disruptive technological advancement in healthcare, offering substantial capabilities to parse vast health datasets, predict complications, and augment clinical decision-making. In maternal health nursing and midwifery, AI applications have expanded rapidly, yet a comprehensive synthesis mapping these modalities within nursing workflows remains absent. This scoping review systematically maps the extant literature surrounding the clinical application of artificial intelligence tools, machine learning models, and natural language processing architectures within the domain of maternal health nursing. Methodology: Following the Joanna Briggs Institute (JBI) methodology for scoping reviews and the PRISMA-ScR guidelines, a systematic literature search was executed across major databases, including PubMed, CINAHL, Scopus, Cochrane Library, and IEEE Xplore, spanning clinical literature published up to May 2026. Studies were screened and included if they explored AI applications integrated within prenatal, intrapartum, or postpartum nursing workflows, monitoring systems, or clinical decision support frameworks. Results: A total of 34 eligible studies were retrieved and analyzed. The evidence base revealed three dominant thematic areas of AI implementation: predictive analytics for hypertensive disorders of pregnancy, gestational diabetes mellitus, and peripartum hemorrhage; intrapartum digital health interventions, featuring automated cardiotocography interpretation and intelligent partograph tracking; and (3) natural language processing platforms or conversational agents targeted at maternal mental health triaging and postpartum lactation education. Conclusion: AI represents a highly promising augmentative framework for maternal health nursing. Realizing its systemic value demands deliberate nursing leadership in algorithmic development, rigorous external cross-validation, and an educational paradigm shift establishing digital competencies within global nursing curricula.