ReviewJournal of clinical ultrasound : JCU2025
Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review.
Review in Journal of clinical ultrasound : JCU, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses 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
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI-guided meta-analysis of non-invasive prenatal testing platforms for trisomy 21 screening: comparative evaluation of cffDNA and fetal cell-based approaches.BMC pregnancy and childbirth · 2026Pooled it
- Applications of artificial intelligence in early childhood health management: a systematic review from fetal to pediatric periods.Frontiers in pediatrics · 2025Pooled it
- Abdominal landmark detection and classification of fetal growth conditions on obstetric ultrasound: a gestational age-conditioned multi-task network.Abdominal radiology (New York) · 2026Article
- Doppler Assessment of the Fetal Brain Circulation.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation.Frontiers in digital health · 2026Review
- Application of artificial intelligence in diagnosis and management of fetal growth disorders: a comprehensive review.Frontiers in medicine · 2025Review
- 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This narrative review examines the integration of Artificial Intelligence (AI) in prenatal care, particularly in managing pregnancies complicated by Fetal Growth Restriction (FGR). AI provides a transformative approach to diagnosing and monitoring FGR by leveraging advanced machine-learning algorithms and extensive data analysis. Automated fetal biometry using AI has demonstrated significant precision in identifying fetal structures, while predictive models analyzing Doppler indices and maternal characteristics improve the reliability of adverse outcome predictions. AI has enabled early detection and stratification of FGR risk, facilitating targeted monitoring strategies and individualized delivery plans, potentially improving neonatal outcomes. For instance, studies have shown enhancements in detecting placental insufficiency-related abnormalities when AI tools are integrated with traditional ultrasound techniques. This review also explores challenges such as algorithm bias, ethical considerations, and data standardization, underscoring the importance of global accessibility and regulatory frameworks to ensure equitable implementation. The potential of AI to revolutionize prenatal care highlights the urgent need for further clinical validation and interdisciplinary collaboration.
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.