The Accuracy of Artificial Intelligence to Support Multimodal Management and Prediction of Gestational Diabetes

Abstract

Gestational Diabetes Mellitus (GDM) is characterized as any degree of glucose intolerance that manifests during pregnancy and typically resolves postpartum. Diagnosis is commonly made through an Oral Glucose Tolerance Test (OGTT); however, there are notable inconsistencies in diagnostic criteria and treatment thresholds both nationally and internationally. The onset of GDM generally occurs in the late second or early third trimester, with potential complications including fetal macrosomia, which may lead to difficult labor, and neonatal hypoglycemia due to excess insulin production in the infant’s pancreas. Furthermore, certain studies suggest that GDM may delay fetal brain development, resulting in long-term neurological impairments. Although artificial intelligence (AI) models have only recently emerged as a potential solution in various medical fields, their application continues to raise concerns, particularly regarding the use and storage of personal data. Machine Learning (ML), a subset of AI, utilizes multivariate classification methods, also known as supervised pattern recognition approaches, which are designed to identify patterns and correlations among multiple variables to categorize them into specific groups or classes. In this context, we are describing here .Thus, artificial intelligence has not yet demonstrated its full clinical potential in the management of gestational diabetes mellitus (GDM). Current applications remain limited in their impact on improving patient outcomes, largely due to methodological heterogeneity and the early stage of implementation. Still, AI has shown considerable promise in the domain of predictive modeling, particularly through its ability to process large volumes of clinical and biochemical data. This analytical capacity enables the identification of complex patterns and correlations that can support the accurate early prediction of GDM, potentially allowing for earlier intervention and personalized care strategies. Future research should focus on validating these predictive models in larger, diverse populations and integrating them into clinical workflows under appropriate regulatory and ethical frameworks.