Journal of Clinical Gynecology and Obstetrics, ISSN 1927-1271 print, 1927-128X online, Open Access
Article copyright, the authors; Journal compilation copyright, J Clin Gynecol Obstet and Elmer Press Inc
Journal website https://jcgo.elmerpub.com

Original Article

Volume 15, Number 3, September 2026, pages 90-96


The Impact of Artificial Intelligence on Clinical Outcomes in Minimally Invasive Gynecological Surgery: A Systematic Review and Meta-Analysis

Figure

↓  Figure 1. The PRISMA flow diagram. PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
Figure 1.

Tables

↓  Table 1. Summary of Key Characteristics of Included Studies (N = 55)
 
CharacteristicCategoryN%Notes
RCT: randomized controlled trial; MIGS: minimally invasive gynecological surgery; CNNs: Convolutional Neural Networks; ML: machine learning; DL: deep learning; AI: artificial intelligence.
Study designRCT1221.8%Cochrane RoB 2 assessed
Observational4378.2%ROBINS-I assessed
Publication year2020–20231730.9%
2024–20263869.1%Rapid growth period
Procedure typeHysterectomy1832.7%Most studied procedure
Endometriosis1425.5%
Myomectomy916.4%
Oncologic814.5%
Other/mixed610.9%
AI modalityComputer vision2443.6%CNNs predominant
Predictive modeling1832.7%ML/DL models
Robotic AI814.5%da Vinci platform
Augmented reality59.1%
Patient populationAI-assisted MIGS23,15047.3%
Conventional MIGS25,80152.7%
Total48,951100%

 

↓  Table 2. Summary of Meta-Analysis Results for Primary Outcomes
 
OutcomeStudies (n)Patients (n)Effect size (95% CI)P valueI2
All analyses used random-effects models.MD: mean difference; OR: odds ratio; CI: confidence interval; I2: heterogeneity statistic.
Operative time4238,204MD: −21.2 min (−28.5 to −13.9)< 0.0000174%
Blood loss3834,712MD: −31.5 mL (−40.2 to −22.8)< 0.0000168%
Complications3129,847OR: 0.65 (0.58 to 0.73)< 0.0000142%
Hospital stay2726,103MD: −0.55 days (−0.71 to −0.39)< 0.0000155%