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Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.

Rawan AlSaad1, Ali Elhenidy2, Aliya Tabassum3

  • 1AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.

Journal of Diabetes Science and Technology
|August 26, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise for improving gestational diabetes mellitus (GDM) prediction and care. Further validation and prospective studies are crucial for clinical integration of these AI tools.

Keywords:
artificial intelligencediabetesgestational diabetespregnancywomen’s health

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Area of Science:

  • Medical Informatics
  • Computational Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Artificial intelligence (AI) offers advanced, data-driven approaches for gestational diabetes mellitus (GDM) care.
  • AI facilitates early detection, personalized management, and intervention strategies for GDM.

Purpose of the Study:

  • To systematically review and synthesize the application of AI models in GDM care.
  • To examine AI's role in screening, diagnosis, management, and outcome prediction for GDM.
  • To analyze study designs, data types, AI models, validation, and performance metrics.

Main Methods:

  • Systematic search of six electronic databases up to February 2025.
  • Inclusion of 126 eligible studies with data extraction and quality appraisal.
  • Modified QUADAS-2 tool used for risk of bias assessment.

Main Results:

  • Most studies (75%) used retrospective designs; 85% focused on GDM prediction.
  • Classical machine learning, particularly logistic regression and random forest, was prevalent (84%).
  • Internal validation was common (68%), but external validation was infrequent (6%); moderate-to-good quality with reporting deficiencies.

Conclusions:

  • AI holds significant potential for enhancing GDM prediction, screening, and management.
  • Wider validation, improved model interpretability, and prospective studies are essential.
  • Translating AI technologies into clinical practice requires addressing current limitations.