Artificial Intelligence in Self-Management of Gestational Diabetes Mellitus: A Systematic Review.

The prevalence of gestational diabetes mellitus (GDM) continues to rise, necessitating reliable and effective self-management strategies to improve maternal and neonatal outcomes. However, current self-management models face challenges, including insufficient data monitoring and analysis, delayed modifications to treatment protocols, and excessive reliance on manual processes. With the expanding application of artificial intelligence (AI) in healthcare, its potential value in the self-management of GDM has attracted increasing attention. This systematic review aimed to synthesize the evidence on the application of AI technologies in the self-management of patients with GDM. This systematic review was conducted in April 2025 and included comprehensive literature searches across PubMed, Embase, The Cochrane Library, Scopus, Web of Science, CINAHL, CBM, CNKI, VIP, and Wanfang databases. The search strategy combined Medical Subject Headings and free-text terms related to GDM, AI, machine learning, and self-management. Quantitative studies that explored the application of AI in the self-management of patients with GDM were included, including randomized controlled trials and cohort studies. Two researchers independently performed study selection and data extraction, followed by quality assessment using risk-of-bias instruments appropriate for each study design. Data were synthesized using a narrative approach combined with thematic synthesis. The initial search yielded 18,973 records. After stepwise screening, 10 studies were included. A total of 645 patients with GDM completed AI-assisted interventions (from 661 initially enrolled), along with 864 control participants (from 877 enrolled). A variety of AI technologies were employed, including expert systems, machine learning, and natural language processing. Their primary functions included abnormality detection and alert triggering, personalized treatment plan generation and adjustment, and data integration and management. The studies reported multiple outcomes. Regarding health outcomes, six studies reported that AI interventions were associated with improved glycemic control, although heterogeneity was observed in delivery outcomes and insulin utilization rates. In terms of adherence, AI interventions tended to increase the frequency of blood glucose monitoring and data upload rates. Regarding system usability, limited data suggested that the accuracy of dietary recommendations and detection of blood glucose abnormalities was satisfactory, whereas the adoption rate of insulin treatment adjustment recommendations was relatively low. User satisfaction was generally high. Facilitators for implementation included technological advantages, user experience, and external support, whereas barriers included data integration and quality issues, technical and hardware or software limitations, patient acceptance, and difficulties in clinical integration. Preliminary evidence suggests that AI may contribute to the self-management of GDM; however, its practical application faces several obstacles. Future efforts should focus on conducting high-quality clinical research and evaluating implementation-related experiences to facilitate the integration of AI into GDM self-management.