ReviewInternational journal of women's health2025
Research Progress and Clinical Implications of Generative Artificial Intelligence in Perinatal Health Care for Advanced Maternal Age Pregnant Women.
Review in International journal of women's health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial intelligence in maternal and child health: Current applications, translational gaps, and future research priorities.Women's health (London, England)Review
Corrections and comments
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To analyze the current application status, technical characteristics, and challenges of Generative Artificial Intelligence (Generative AI) in perinatal health care for advanced maternal age pregnant women and explore targeted optimization strategies. Methods: A systematic literature review was conducted by searching PubMed, Web of Science, CNKI, and Wanfang Data from January 2020 to April 2025. Studies were included if they focused on Generative AI applications in perinatal care for women aged ≥35 years; 78 eligible studies (42 Chinese, 36 international) were finally included, covering technical applications, clinical validation, and ethical governance. We summarized the applications of Generative AI in risk prediction, personalized management, and remote monitoring, and analyzed issues related to data governance, technical limitations, resource allocation, and ethical supervision. Results: Generative AI improves healthcare efficiency by integrating multiple data sources for model construction, planning dynamic interventions, and facilitating remote monitoring. Specifically, GANs-based models achieve an AUC of 0.80-0.85 in predicting Group B Streptococcus infection, while Transformer models enhance the accuracy of prenatal depression screening by 15-20% compared to traditional methods. However, it faces challenges including data privacy risks (eg, 32% of maternal health institutions lack encrypted data storage), the "black box" nature of models (42% of clinicians report low trust in AI decision-making), urban-rural technological gaps (only 18% of county-level hospitals use AI perinatal tools), and ambiguous liability definitions. Conclusion: Generative AI demonstrates significant application potential in perinatal care for advanced maternal age pregnant women. Promoting its implementation through technological innovation (eg, explainable AI), interpretability optimization, resource deployment (eg, lightweight mobile tools), and ethical supervision is crucial to improving maternal and infant health outcomes in China and globally.
Indexed as
Identifiers
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.