ReviewInternational journal of women's health2024
A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery.
Review in International journal of women's health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in personalized computed tomography dose optimization: a systematic review of techniques and clinical outcomes.Frontiers in medicine · 2026Pooled it
- Beyond the 'Pregnancy Black Box': a global roadmap for artificial intelligence-driven pharmacogenomics in maternal-neonatal health.The pharmacogenomics journal · 2026Review
- The economic imperative of artificial intelligence in maternal and neonatal health: a review of evaluation benefits, frameworks, challenges, future perspectives, and limitations.Cost effectiveness and resource allocation : C/E · 2026Review
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Using artificial intelligence as a technological tool in gynecologic and obstetric health: A narrative literature review.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Review
- Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation.Frontiers in digital health · 2026Review
- Transforming perinatal health with AI-predictive models in precision and digital innovations for maternal and fetal well-being: a systematic review.Oxford open digital health · 2026Article
- Benchmarking ChatGPT and Other Large Language Models for Personalized Stage-Specific Dietary Recommendations in Chronic Kidney Disease.Journal of clinical medicine · 2025Article
- Integrating Artificial Intelligence into Perinatal Care Pathways: A Scoping Review of Reviews of Applications, Outcomes, and Equity.Nursing reports (Pavia, Italy) · 2025Review
- Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review.Journal of clinical ultrasound : JCU · 2025Review
- Pre‑partum blood leukocyte profiles distinguish gestational inflammatory stages that predict birth‑related adverse outcomes.Frontiers in immunology · 2025Article
- The role of AI in reducing maternal mortality: Current impacts and future potentials: Protocol for an analytical cross-sectional study.PloS one · 2025Article
- Biases in Artificial Intelligence Application in Pain Medicine.Journal of pain research · 2025Review
- Artificial intelligence and sexual reproductive health and rights: a technological leap towards achieving sustainable development goal target 3.7.Reproductive health · 2024Article
- Review
- Revolutionizing Maternal Health: The Role of Artificial Intelligence in Enhancing Care and Accessibility.Cureus · 2024Review
- Review
- Artificial intelligence in maternal and child health: Current applications, translational gaps, and future research priorities.Women's health (London, England)Review
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
The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.
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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.