Peer-reviewed veterinary case report
Using AI to review surgery options for vaginal vault prolapse
By Park Y et al.·2026·Department of Obstetrics and Gynecology, South Korea·View original on Europe PMC →
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Original publication title: Large language models in systematic review and meta-analysis of surgical treatments for vaginal vault prolapse.
Plain-English summary
This study looked at how well a large language model, like ChatGPT, could help researchers review surgical treatments for vaginal vault prolapse, a condition affecting women. The researchers found that the model could quickly and accurately screen studies and extract data, achieving high levels of agreement with traditional methods. They compared different surgical techniques and found that sacrocolpopexy had better success rates than sacrospinous fixation, while transvaginal mesh showed improved success over sacrospinous fixation but required more follow-up surgeries. However, the researchers concluded that there wasn't enough strong evidence to say that one surgical method was clearly better than the others. Overall, the study suggests that using large language models can make the research process more efficient without losing accuracy.
Abstract
Systematic reviews provide the highest level of evidence but remain resource-intensive. We evaluated the performance of a large language model (LLM; ChatGPT, OpenAI) in a PRISMA-guided review of randomized controlled trials on vaginal vault prolapse surgery. Prompts were carefully designed to minimize errors, and outputs were verified. Each task was completed within minutes. For title/abstract screening, recall was 69.8% and precision 85.7% (κ = 0.77); full-text agreement 94.1-100% (κ = 0.82-1); data extraction accuracy 87.5-99.7%. From 18 RCTs (1668 women), sacrocolpopexy (SC) showed higher anatomic success than sacrospinous fixation (SSF) (OR 1.42, 95% CI 0.71-2.84). Transvaginal mesh improved 3-year objective success compared with SSF (OR 1.84, 95% CI 1.13-2.99) but had higher reoperation rates (5-16% vs 2-4%) than SC. We did not find conclusive evidence that any single technique is superior; most comparisons were underpowered, with wide confidence intervals and substantial heterogeneity. All LLM-derived statistical results were identical to those from conventional R analyses, confirming robustness. Validated LLM workflows can enable more efficient and scalable evidence synthesis.
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Search related cases →Original publication on Europe PMC: https://europepmc.org/article/MED/41714807