ReviewCureus2026
Artificial Intelligence in Obstetrics and Gynecology Nursing: Clinical, Educational, and Ethical Perspectives.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is rapidly transforming healthcare by enabling advanced data analysis, predictive modeling, and intelligent clinical decision support systems. In obstetrics and gynecology (OBG) nursing, AI technologies are increasingly recognized as valuable tools for improving maternal and women's healthcare outcomes. These technologies facilitate early identification of high-risk pregnancies, enhance fetal monitoring accuracy, and support gynecologic cancer screening. This narrative review examines current evidence on the emerging applications of AI in obstetric and gynecologic practice, with particular emphasis on its relevance to nursing roles and responsibilities. A structured search of electronic databases, including PubMed, Scopus, Excerpta Medica Database (EMBASE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Web of Science, was conducted for studies published between January 2010 and July 2025. The search yielded 612 records, of which 62 studies met the inclusion criteria and were included in the thematic synthesis. Key domains explored include predictive analytics for maternal risk assessment, AI-assisted clinical decision support systems for labor and emergency management, wearable and remote monitoring technologies for continuous maternal and fetal surveillance, and image-based diagnostic tools used in gynecologic oncology screening and early disease detection. The review also highlights applications of AI in nursing education, including adaptive learning platforms and simulation-based training that enhance clinical reasoning and preparedness for obstetric emergencies. Ethical and implementation challenges, including algorithmic bias, data privacy, transparency, and equitable access, are also discussed. While AI shows promising potential to improve diagnostic accuracy, support evidence-based decision-making, and optimize workflow efficiency, much of the current evidence remains in developmental or pilot phases, with limited large-scale validation. Overall, AI has the potential to strengthen obstetrics and gynecology nursing practice by facilitating proactive, data-driven care while preserving the essential human-centered and compassionate nature of nursing in maternal and women's health.
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What OpenQuestion 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 OpenQuestion graph.