The Implementation of Artificial Intelligence in the Management of Gestational Diabetes Mellitus
DOI:
https://doi.org/10.55892/jrg.v9i20.2886Keywords:
risk assessment, gestational diabetes mellitus, innovation, artificial intelligence, maternal healthAbstract
Gestational Diabetes Mellitus (GDM) is characterized by hyperglycemia resulting from insulin resistance or impaired insulin secretion during pregnancy, influenced by placental hormones and adipokines. With a prevalence of up to 25% worldwide and approximately 18% in Brazil, it is associated with maternal and fetal complications. In developing countries, inadequate prenatal care reinforces the importance of early diagnosis. Traditional screening methods have limitations, and Artificial Intelligence (AI) has emerged as a promising tool for the screening and management of GDM. Therefore, the aim of this article was to analyze the impact of AI on the early diagnosis, management, and treatment of GDM. To this end, a qualitative and descriptive integrative review was conducted in six stages, guided by a research question formulated using the PICO strategy. The search was carried out in the PubMed and VHL databases using the descriptors “Artificial Intelligence” and “Gestational Diabetes,” including original studies published between 2020 and 2025, in English, Portuguese, and Spanish, with free full-text access and aligned with the Brazilian context. A total of 84 articles were identified, of which 27 comprised the final sample. An increase in publications was observed, especially in 2022 and 2023, with a predominance of international studies, mainly from China. The most frequent approaches employed machine learning algorithms (XGBoost, neural networks, AutoML) for GDM prediction, identification of risk factors, and prediction of neonatal outcomes. Some studies also proposed mobile applications for screening and management. Therefore, AI shows potential to improve the early prediction and management of GDM, providing more accurate and personalized diagnoses. However, despite the promising results, external validation, methodological standardization, and policies to ensure the ethical and equitable integration of these tools into prenatal care are still needed.
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References
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