ReviewArchives of gynecology and obstetrics2024
Exploring the potential of machine learning in gynecological care: a review.
Review in Archives of gynecology and obstetrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed, 17 citations in OpenAlex.
- Machine learning based classification in obstetrics: evaluating models, partitioning strategies, and key predictors in cardiotocography.BMC pregnancy and childbirth · 2026Article
- Prediction of preterm and low birth weight risk using a physiology based artificial neural network integrating hematological, dental, and periodontal index markers: a cross sectional study based on machine learning.BMC pregnancy and childbirth · 2026Article
- Performance of next-generation AI chatbots in gynecological knowledge assessment: a comparative pilot study of ChatGPT-5, Gemini-3, DeepSeek-V3.2, and Claude-4.5-Opus.Archives of gynecology and obstetrics · 2026Article
- Application of machine learning to predict delayed fecundability among women in sub-Saharan Africa.Reproduction & fertility · 2025Article
- The Role of Artificial Intelligence in Female Infertility Diagnosis: An Update.Journal of clinical medicine · 2025Review
- Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer.Frontiers in endocrinology · 2025Article
- Large language models to facilitate pregnancy prediction after in vitro fertilization.Acta obstetricia et gynecologica Scandinavica · 2025Article
- Prospective Applications of Artificial Intelligence In Fetal Medicine: A Scoping Review of Recent Updates.International journal of general medicine · 2025Review
- Evaluation of menstrual blood loss (MBL) by self-perception and pictorial methods and correlation to uterine myometrial pathology.Archives of gynecology and obstetrics · 2024Article
- ChatGPT use in daily obstetrics and gynecology practice: clarification and recommendations.Archives of gynecology and obstetrics · 2024Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. This paper investigates how machine learning (ML) algorithms are employed in the field of gynecology to tackle crucial issues pertaining to women's health. This paper also investigates the integration of ultrasound technology with artificial intelligence (AI) during the initial, intermediate, and final stages of pregnancy. Additionally, it delves into the diverse applications of AI throughout each trimester.This review paper provides an overview of machine learning (ML) models, introduces natural language processing (NLP) concepts, including ChatGPT, and discusses the clinical applications of artificial intelligence (AI) in gynecology. Additionally, the paper outlines the challenges in utilizing machine learning within the field of gynecology.
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.