Eficacia de los gemelos digitales en el seguimiento y prevención de complicaciones en el embarazo frente a los modelos tradicionales de consultas subsecuentes: Revisión sistemática

Abstract

Introduction: A digital twin is a virtual replica of a real-world object, process, or service. It is based on advanced data and algorithms, with features that surpass traditional simulations. Objectives: To evaluate the effectiveness of using digital twins in monitoring and preventing complications during pregnancy compared to traditional models of follow-up consultations. Methodology: Studies published within the last 5 years, in either Spanish or English, with open access, conducted on humans over 18 years of age, and that were clinical trials, observational, multicenter, or comparative studies were included. Case reports, systematic reviews, and gray literature were excluded. Searches were conducted in PubMed and Scopus between November 1, 2024, and April 30, 2025. RoB 2, ROBINS-I, and JBI tools were used for cross-sectional and narrative studies. A thematic analysis and comparative tables were used to synthesize clinical, technological, and preventive findings. Results: Twenty-two studies were included, among which clinical trials, observational, and cross-sectional studies from different countries can be highlighted. Certain studies showed superiority over traditional models, particularly in mental health, hypertense control, and reduction of unnecessary visits. Discussion: Limitations include methodological heterogeneity and potential bias in non-randomized studies. However, digital technologies are promising tools in high-risk pregnancies or contexts with limited access. Conclusions: Digital twins complement and, in some cases, exceed traditional models in prenatal monitoring.