Artificial intelligence technologies in pregnancy and childbirth care: a scoping review from a nursing perspective.

PURPOSE: This scoping review examined the applications of artificial intelligence (AI) technologies in pregnancy and childbirth care, including patterns of use and outcomes relevant to nursing practice.

METHODS

Using Arksey and O'Malley's methodological framework, we comprehensively searched PubMed, CINAHL, Embase, and Web of Science for empirical studies published from 2015 to 2025. Of the 1,282 records identified, 1,191 were screened after duplicates were removed, and 20 studies of pregnancy and childbirth care were included.

RESULTS

AI applications in the 20 included studies were grouped into four domains: risk prediction models (n=14), fetal/intrapartum monitoring (n=3), clinical decision support (n=1), and patient education and nursing documentation (n=2). Publications increased markedly from 2021 onward. Machine learning algorithms, including random forest, support vector machine, extreme gradient boosting, and logistic regression, were used mainly to predict obstetric complications such as preterm birth, hypertensive disorders of pregnancy, preeclampsia, and gestational diabetes mellitus. AI models also analyzed fetal heart rate and electrocardiogram signals to support intrapartum decisions, including prediction of episiotomy risk and vaginal birth after cesarean delivery. A robot-assisted education system supported patient education, and a large language model supported nursing documentation.

CONCLUSION

AI is used primarily for risk prediction and clinical decision support in pregnancy and childbirth care, with emerging applications in patient education and psychosocial assessment. Evidence for nurse-led AI interventions is limited. Research should prioritize clinical validation, AI literacy education, nurse-led program development, and ethical guidelines to support safe, equitable, individualized perinatal nursing care.