ReviewActa obstetricia et gynecologica Scandinavica2026
Medical education in obstetrics and gynecology: A global update from 2025.
Review in Acta obstetricia et gynecologica Scandinavica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Enthusiasm to learn and standardization: Outdated concepts?Acta obstetricia et gynecologica Scandinavica · 2026Article
- Implementation and evaluation of customized artificial intelligence personal tutor (chat GPT physiology companion) for medical students in the reproduction module.BMC medical education · 2026Article
- Editorial: Education in obstetrics and gynecology: 2025.Frontiers in medicine · 2026Article
- Medical education in obstetrics and gynecology: A global update from 2025.Acta obstetricia et gynecologica Scandinavica · 2026Review
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
As medical knowledge and technologies rapidly evolve, curricula have become increasingly dense, and designing effective OB-GYN education that prepares learners for diverse medical careers within limited timeframes is a global challenge. This review provides an international overview of contemporary medical education in obstetrics and gynecology (OB-GYN) across undergraduate, postgraduate, and continuing professional development levels. A narrative review of recent peer-reviewed literature, international guidelines, and global initiatives (2023-2025) was conducted, identifying key innovations, trends, and challenges in OB-GYN education worldwide, with a focus on curriculum reforms, competency-based education, simulation, telemedicine, AI applications, global standardization, and equity-oriented initiatives. Undergraduate OB-GYN curricula are increasingly standardized, integrating core competencies, early clinical exposure, and reproductive health. Postgraduate training adopts competency-based frameworks, enhanced by simulation, virtual reality, and tele-education, while continuing medical education has shifted toward flexible digital platforms and structured credentialing. Innovations, such as AI-driven learning tools, simulation drills, and telemedicine-based training, have improved skill acquisition, and global bodies, such as FIGO, RCOG, and ACOG, promote curriculum harmonization and equity. The COVID-19 pandemic accelerated digital adoption but revealed gaps in surgical training and support. Overall, OB-GYN education is in a transformative phase, marked by technology, standardization, and equity, yet significant disparities persist, especially in resource-limited settings. Continued global collaboration, investment in educational infrastructure, and adaptive curriculum development are essential to prepare OB-GYN professionals for evolving clinical demands and healthcare inequities in the postpandemic era.
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.