From data to diagnosis: a systematic review on AI-driven approaches to diabetes prediction

Diabetes mellitus is one of the most pressing global health challenges, and early prediction is critical to reducing complications, mortality, and healthcare costs. Conventional diagnostic tools remain limited, as they often rely on a small set of biomarkers and fail to capture lifestyle, genetic, or environmental risk factors. This review systematically evaluates how artificial intelligence (AI), including machine learning (ML) and deep learning (DL), enhances diabetes prediction by integrating multimodal data and improving clinical interpretability. Following PRISMA 2020 guidelines, a systematic search was conducted across PubMed, Scopus, IEEE Xplore, Web of Science, and ACM Digital Library for studies published between 2010 and 2024. Inclusion criteria required AI-based diabetes prediction models with reported performance metrics. From 2134 records, 155 studies met the criteria and were synthesized. AI models consistently outperformed conventional approaches (60–75% accuracy), Ensemble methods such as Random Forests and XGBoost achieved accuracy of 85–90% and AUC-ROC values > 0.90 (Abnoosian in BMC Bioinf 24:373, 2023. https://doi.org/10.1186/s12859-023-05465-z; Chen and Guestrin in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, Association for Computing Machinery, 2016. https://doi.org/10.1145/2939672.2939785). DL architectures showed notable strengths in unstructured data: CNNs achieved AUC > 0.95 in retinal image analysis (Gulshan et al. in JAMA 316(22):2402–2410, 2016. https://doi.org/10.1001/jama.2016.17216; Kermany et al. in Cell 172(5):1122–1131, 2018. https://doi.org/10.1016/j.cell.2018.02.010), while LSTMs reached 85–90% accuracy in continuous glucose monitoring predictions (De Bois et al. in Prediction-coherent LSTM-based recurrent neural network for safer glucose predictions in diabetic people, arXiv:2009.03722, 2020; Bian et al. in PLoS ONE 19(9):e0310084, 2024. https://doi.org/10.1371/journal.pone.0310084). Emerging methods such as federated learning enable privacy-preserving cross-institutional collaboration with comparable accuracy (~ 88%), while explainable AI techniques (e.g., SHAP, LIME, attention mechanisms) enhance transparency and clinical trust. Real-world case studies demonstrate improvements in early detection, reduced hospitalizations, and increased patient engagement when AI models are integrated into EHRs or mobile health apps. This review contributes a novel synthesis by combining methodological insights with clinical applications. The central takeaway is that AI-driven diabetes prediction offers significant advantages over traditional methods, but challenges in data quality, generalizability, fairness, and regulatory compliance remain. Addressing these will be essential to ensure safe, equitable, and clinically meaningful adoption in healthcare practice.